Air-conditioning device

ABSTRACT

Temperatures in a Dr side air-conditioning zone and a Pa side air-conditioning zone are controlled highly independently of each other without temperature interference between each zone. A room internal air temperature sensor and a room external air temperature sensor are provided. Dr side and Pa side temperature setters separately set room setpoint temperatures (Tset(Dr), Tset(Pa)) in each zone. First and second target blow-out temperature calculating portions, which include neural network, input the room setpoint temperatures and the temperature data. Then it calculates Dr side and Pa side target blow-out temperatures (TAO(Dr), TAO(Pa)) relative to each air-conditioning zones by using a neural network. Air-mixing doors separately adjusts the temperatures of conditioned air blown out from Dr side air passage and Pa side air passage to be the first and second target blow-out temperatures. Here, the neural network has the learning function, which adjusts its output to be desired data (teacher signal). Therefore, the output at a specific input condition can be adjusted without temperature interference between each zone.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon Japanese Patent Applications No. Hei.10-112435 filed Apr. 22, 1998, No. Hei. 10-115419 filed Apr. 24, 1998,No. Hei. 10-115420 filed Apr. 24, 1998, and No. Hei. 10-117416 filedApr. 27, 1998, the contents of which are incorporated herein byreference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an air-conditioning device forautomatically controlling the temperature in a room such as a passengercomponent of a vehicle or a room in a building.

2. Description of Related Art

Some air-conditioning devices for controlling two or more differentair-conditioning zones independently of each other have heretofore beenproposed in an air-conditioning device for automobile field. When thetemperatures, in a driver seat (Dr) side air-conditioning zone and in apassenger seat (Pa) side air-conditioning zone, are controlledindependently, since there is no partition wall between twoair-conditioning zones, temperature interference between the twoair-conditioning zones may occur.

As an air-conditioning device for automobiles, which is capable ofcontrolling independently between the two air-conditioning zones,Japanese Laid-open Patent No. 7-32854 has proposed. In thisair-conditioning device, when a Dr side target blow-out temperature anda Pa side target blow-out temperature are calculated, a calculation termof difference between a Dr side setpoint temperature and a Pa sidesetpoint temperature are corrected by correction gain, which is decidedbased on an external temperature, so as to realize desired temperaturesin each of the Dr side and Pa side air-conditioning zones.

This air-conditioning device aims to prevent practical temperatures ofeach zones from deviating from predetermined setpoint temperatures dueto an influence of the external temperature, by the correction describedthe above.

However, the temperature interference between the two air-conditioningzones can not be conjectured only based on the external temperature andthe difference between the two setpoint temperatures. Actually, thetemperature interference is related to an internal temperature, ablow-out temperature, and an amount of air or the like at every timing.Therefore, the independent temperature controlling can not be operatedaccurately by only the correction described above.

FIGS. 12A, 12B are temperature characteristics of the independentcontrolling, which are experimentally confirmed. FIG. 12A shows acharacteristic of a temperature of area in which surrounding passengerswhen the Pa side setpoint temperature is set to constant and the Dr sidesetpoint temperature varies from 22° C. to 28° C. FIG. 12B shows acharacteristic of opposite relation.

As shown in FIG. 12A, during a varying of the setpoint temperature of Drside, the temperature interference, which is a phenomenon, that thetemperature of area surrounding passengers in Pa side is dragged bytemperature changes of Dr side. Hence, a controllability of temperatureof both Dr side and Pa side has no inconvenient.

However, as shown in FIG. 12B, when the setpoint temperature of the Paside is varied, the temperature of area surrounding passengers in Paside is decreased slightly with respect to normal temperature increasingdue to an influence on Dr side. Specifically, when the Pa side setpointtemperature (Tset(Pa))=28° C., the temperature of area surroundingpassengers in Pa side reaches only around 25.5° C.

In the Japanese Laid-open Patent No. 7-32854, for the purpose ofoffsetting the temperature interference, a correction value iscalculated by multiplying the difference between two setpointtemperatures, and is added to the target blow-out temperature. FIGS.13A, 13B are temperature characteristics when this correction isadopted. As shown in FIG. 13B, when the Pa side setpoint temperaturevaries, the control characteristic at the condition where Tset(Pa)=28°C. is improved, however, the correction term influences other conditionsto the contrary. This is because the correction term depends on thedifference between two setpoint temperatures.

In other words, from the temperature difference point of view, both thecondition of which the characteristic should be improved (Dr sidesetpoint temperature Tset(Dr)=25° C., Pa side setpoint temperatureTset(Pa)=28° C.) and the condition of which the characteristic should bemaintained (Tset(Dr)=25° C., Tset(Dr)=22° C.) are the identical (each ofthem is 3° C.). Therefore, the correction is adapted to other condition.

Then, a disadvantage occurs because the temperature of area surroundingpassengers in Pa side is decreased below 22° C., as shown in FIG. 13B,in the condition of which the characteristic should be maintained(Tset(Dr)=25° C., Tset(Dr)=22° C.) may occur.

Similarly, as shown in FIG. 13A, a disadvantage occurs because thetemperature of area surrounding passengers in Pa side is deviated from25° C. due to temperature varying in Dr side may occur.

FIG. 14A shows a characteristic of a Dr side correction gain KDraccording to the related art described the above. When the externaltemperature rises from T1 to T2, KDr decreases from K1 to K2. A Pa sidecorrection gain Kpa has a similar characteristic. If the Pa sidecorrection gain Kpa is changed from K3 to K4 at external temperature=10°C., since the relation Kpa=K4 is adopted to other condition duringexternal temperature=10° C., the disadvantage shown in FIGS. 13A, 13Bmay occur.

Therefore, in order to eliminate the disadvantage, it is necessary tochange the Pa side correction gain Kpa to K4 in only a particularcondition, and to maintain the Pa side correction gain with K3 withoutchange in the other conditions.

In other words, a control logic, which can change the correction gain inonly the particular condition, is needed. However, environmentconditions, of which the air-conditioning device for automobiles faces,include a wide variety of parameters such as the external temperature,an amount of solar radiation (hereinafter, radiation amount), a speed ofthe automobile and the like. Therefore, it is extremely difficult toinvestigate a relationship of factors at which these environmentconditions influence to the temperature control characteristic one byone, to quantify the influence of the relationship, and to decide theblow-out temperature control logic corresponds to the influence, becauseit needs huge processes.

On the other hand, in another conventional automatically controlair-conditioning device for vehicles, as shown in Japanese Laid-openPatent No. 6-195323, calculates an air amount by using a neural networkbased on an internal air temperature and an external air temperature ofthe vehicle, a setpoint temperature, and a radiation amount.

In this kind of air-conditioning device, during a normal operation afterthe internal air temperature reaches the setpoint temperature, when ablow-out port mode is either in a FACE mode or in a BI-LEVEL (B/L) mode,the air amount is increased in proportion to the radiation amount so asto increase a cooled air feeling (felt by a driver or a passenger),during high solar radiation.

Here, when the blow-out port mode is in a FOOT mode, since thetemperature in a passenger component rises due to the radiation, anincrease of amount of conditioned air (hereinafter, air amount) is notneeded. Therefore, the air amount is not increased in proportion to theradiation amount.

According to the above-mentioned conventional device, when the airamount is changed in proportion to radiation during a normal operation,the following disadvantage may occur.

The number of output of the air amount, which is calculated by theneural network, is only one independent of the blow-out port mode.Therefore, when the blow-out port mode is switched among the FACE mode,the B/L mode and the FOOT mode, the air amount needs to be changed stepby step during high radiation.

This disadvantage will be explained in detail with reference to FIG. 25.In FIG. 25, the ordinate represents a blower voltage which determinesthe air amount to the passenger component, the abscissa represents adifference TD (=Tr−Tset) between the internal air temperature Tr in thepassenger compartment and the setpoint temperature Tset. This differenceTD is zero around center of an area A on the abscissa, and is in a plusat the right side on the abscissa and in a minus at the left side on theabscissa.

In the FOOT mode, when the difference TD is in around zero in the normaloperation area A, the blower voltage is set to the minimum voltage E₂independent of radiation. On the other hand, in the FACE mode or the B/Lmode, the blower voltage is increased from E₂ to E₄ in proportion toradiation. Since this changes (increasing), which is an amount ofchanges ΔE of the blower voltage due to mode switching, does not havecontinuously (step by step changes), a learning of the neural networkbecomes difficult.

Further the conventional device inputs data such as the internal airtemperature, the external temperature, the setpoint temperature and theradiation amount to the neural network. The total number of data isdesired to reduce so as to reduce the number of intermediate layers andneurons in the neural network, and to reduce the total calculation timeof the neural network enough to converge the learning of linkcoefficients between the neurons.

A further conventional automatically control air-conditioning device forvehicles, as shown in Japanese Laid-open Patent No. 56-86815, calculatesa target blow-out temperature TAO, which is used for maintaining atemperature in a passenger component. Then it controls a temperatureadjuster (e.g., air-mixing door or hot water valve) so that atemperature of air blown to the passenger compartment approaches thetarget blow-out temperature TAO. The target blow-out temperature TAO iscalculated as follows:

TAO=Kset×Tset−Kr×Tr−Kam×Tam−Ks×Ts+C

Here, Tr is an internal air temperature, Tam is an external airtemperature, Ts is a radiation amount to the passenger compartment, Ksetis a temperature set gain, Kr is an internal air temperature gain, Kamis an external air temperature gain, Ks is a radiation amount gain, andC is an correction constant value.

One of a Face mode for blowing air to a face area of the passenger, aFOOT mode for blowing air to a foot area of the passenger, and bi-level(B/L) mode for blowing air to both the face area and the foot area ofthe passenger, is selected based on the target blow-out temperature TAO.

FIGS. 46A-46C show blow-out port control based on the TAO. The blow-outport mode is changed to the FACE mode→the B/L mode→the FOOT mode, inproportion to a rising of TAO.

According to this conventional device, if heat load conditions for thevehicle are same, the TAO will be same value. Therefore, in this case,the blow-out port mode will be set to be same mode. However, a heatfeeling of the passenger due to surrounding condition is different fromthe heat load condition. Hence, a uniform switching of the blow-out portmode based on the TAO may make an air-conditioning feeling worse.

These air-conditioning feeling will be explained with reference to FIGS.46A-46C. FIG. 46A shows a condition of the external air temperature Tamis 10° C. (rather warm), and cloudiness (less solar radiation). FIG. 46Bshows a condition of the external air temperature Tam is 0° C. (rathercold), and fairy (much solar radiation). In these two conditions, bothof the TAO will be the same value “a”, therefore, the FOOT mode isselected uniformly.

However, in the case of FIG. 46B, the passenger will feel hot due to theradiation to the upper body, even if the external air temperature israther low, and will want more cooled air to the upper body. That is, inthis case, it is desired to select the B/L mode to improve theair-conditioning feeling. Therefore, the conventional device could notcontrol the blow-out port mode in view of the radiation.

Furthermore, the external air temperature and a temperature of hot waterto a heat exchanger also influence the air-conditioning feeling.However, the conventional device also could not control the blow-outport mode in view of these factors.

In order to resolve the above-mentioned inconvenience, it can be thoughtthe following structure as shown in FIGS. 47A, 47B. That is, two mapsincluding a no radiation map (FIG. 47A) and a radiation map (FIG. 47B)are provided as a characteristic switching map between the blow-out portmode and the TAO. when it is in the radiated condition, as shown in FIG.47B, a switching point of the B/L mode will be changed to a hightemperature side. Similarly, the same method can be adopted for theexternal temperature, and the hot water temperature.

However, the structure in FIGS. 47A, 47B may increase memory portion(ROM) of an air-conditioning electrical control unit, because it needsadditional maps.

Further, environment conditions, of which the air-conditioning devicefor automobiles faces, include a wide variety of parameters such as theexternal temperature, an amount of solar radiation, a speed of theautomobile and the like. Therefore, it is extremely difficult toinvestigate a relationship of factor at which these environmentconditions influence the temperature control characteristic one by one,to quantify the influence of the relationship, and to decide theblow-out temperature control logic corresponds to the influence, becauseit needs huge processes.

Then, another disadvantage of Japanese Laid-open Patent No. 56-86815will be explained. In this conventional device, the amount radiation Tsis included as a calculation term in the equation of the target blow-outtemperature TAO. Therefore, even if it is at the timing just after theair-conditioning device starts in winter (warm-up), TAO is calculated aslow temperature. Then, a warm-up time may be long time. The warm-up timecorresponds to a period between the temperature adjuster is adjustedfrom maximum heating position to temperature region and a roomtemperature rises to the setpoint temperature.

In order to solve the above-mentioned disadvantage, Japanese Laid-openPatent No. 4-163223 is proposed. In this device, when a temperaturedifference (Tr−Tset) between the internal air temperature Tr of thepassengers component and the setpoint temperature Tset is minus, theradiation amount Ts as the calculation term (amount on radiationcorrection) is decreased in proportion to an increasing of the absolutevalue of the temperature difference.

According to an investigation, it is found the following facts. That is,when the radiation amount correction is decided only based on thetemperature difference (Tr−Tset), it may be impossible to calculate theradiation amount correction for various surrounding conditions. That is,even if the temperature difference is equal, the TAO is desired to behigh temperature by decreasing the radiation amount correction when theexternal air temperature is extremely low like in winter, so as toshorten the warm-up time.

Similarly, even if the temperature difference is equal, the TAO isdesired to be low temperature by decreasing the radiation amountcorrection when there is little solar radiation, so as to shorten thewarm-up time.

In winter, since an angle of the sun is rather small, the solarradiation is likely to be radiated to upper body of the passenger. Insuch a case, when the internal air temperature reaches the setpointtemperature (Tr−Tset≈0) as the result of heating, an operation of willbe normal operation. Then, the passenger may feel hot due to theradiation. Therefore, it is desired to set TAO low temperature byincreasing the radiation amount correction during much radiation in thenormal operation so as to set the blow-out port mode to B/L mode to blowcooled air from a face blow-out port.

SUMMARY OF THE INVENTION

The present invention was accomplished in view of the above-mentionedcircumstances. First object is to provide an air-conditioning device forcontrolling the air-conditioning temperatures in a firstair-conditioning zone and in a second air-conditioning zone highlyindependently of each other by restricting temperature interferencebetween the first air-conditioning zone and the second air-conditioningzone.

A neural network, which is one of information process system, has acharacteristic to correct its output to be desired data (teacher signal)automatically, by adjusting link coefficients (synapse weights) betweeneach neurons in each layers in the neural network automatically (i.e.,learning function). The present inventions aim at correcting the targetblow-out temperature only at a specific condition without increasing anengineer's process, by using the automatic adjusting function of linkcoefficients between the neurons in the neural network.

Furthermore, second object is to provide an air-conditioning device,which calculate an air amount by using a neural network, of whichlearning can be simplified.

Also, third object is to provide an air-conditioning device, whichcalculate an air amount by using a neural network, of which totalcalculation time can be decreased.

Furthermore, fourth object is to provide an air-conditioning device,which can control a blow-out port mode finely in accordance toair-conditioning feeling of user.

Furthermore, fifth object is to provide an air-conditioning device,which can calculate a radiation amount correction accurately to improvean air-conditioning feeling of user.

In order to accomplish one of the above-mentioned object, the presentinvention provides an air-conditioning device includes a first and asecond temperature adjusters, and a first and a second target blow-outtemperature calculating portions for input setpoint temperatures(Tset(Dr), Tset(Pa)) of a first and a second air-conditioning zones, aninternal air temperature (Tr) detected by a temperature data detector,and an external air temperature (Tam) detected by the temperature datadetector, to calculate a first and a second target blow-out temperatures(TAO(Dr), TAO(Pa)) of each air-conditioning zones by using a neuralnetwork. Here, the first and the second temperature adjusters arecontrolled so that the blow-out temperatures of air-conditioned air fromeach air passages relative to the first and the second air-conditioningzones can correspond to the first and the second target blow-outtemperatures (TAO(Dr), TAO(Pa)).

According to the present invention, the first and the second targetblow-out temperatures (TAO(Dr), TAO(Pa)) related to eachair-conditioning zones are calculated via the neural network. The neuralnetwork has the learning function, which adjusts the link coefficients(synapse weights) between each neurons in each layers in the neuralnetwork automatically to correct its output to be desired data (teachersignal). Therefore, the output at a specific input condition can beadjusted, by changing the teacher signal at the specific input conditionand then adjusting the link coefficients (synapse weights) automaticallyin advance.

Furthermore, since the neural network adjusts its whole linkcoefficients so that the desired outputs (teacher signal) at the otherinput condition are maintained even if the output at the specific inputcondition is changed. Therefore, the change of the output at thespecific input condition does not influence the outputs at the otherinput conditions.

Hence, when the setpoint temperatures of the first and the secondair-conditioning zones are changed, both temperatures of the areasurrounding passengers in the first and the second air-conditioningzones are highly independent controlled with accurately, without thetemperature interference between each zones.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating the constitution of whole system of anair-conditioning device according to a first embodiment of the presentinvention;

FIG. 2 is a block diagram of main functions of the first embodiment;

FIG. 3 is a schematic diagram of a neural network of temporary targettemperature calculating portion of the first embodiment;

FIGS. 4A, 4B are diagram and graph for explaining a output calculatingprocedure of neural network;

FIG. 5 is a schematic diagram of a neural network of radiation amountcorrection calculating portions of the first embodiment;

FIGS. 6A-6C are diagram of characteristics illustrating the radiationamount correction calculating portions;

FIG. 7 is a schematic diagram of a neural network of blow-out port modecalculating portion of the first embodiment;

FIG. 8 is a schematic diagram of a neural network of air amountcalculating portion of the first embodiment;

FIG. 9 is a flow chart illustrating a control flow according to thefirst embodiment;

FIG. 10 is a diagram of characteristics illustrating a calculation ofthe blow-out port mode according to the first embodiment;

FIGS. 11A, 11B are diagrams of control characteristics of temperaturesof area surrounding passengers in Dr side and Pa side according to thefirst embodiment;

FIG. 11C is a diagram illustrating a relationship between input signalsand output signal of neural network;

FIGS. 12A, 12B are diagrams of control characteristics of temperaturesof area surrounding passengers in Dr side and Pa side;

FIGS. 13A, 13B are diagrams of control characteristics of temperaturesof area surrounding passengers in Dr side and Pa side according torelated art;

FIGS. 14A, 14B are diagrams illustrating a setting procedure ofcorrection gain of a target blow-out temperature according to therelated art;

FIGS. 15A, 15B are diagrams illustrating a setting procedure ofcorrection gain according to the related art;

FIG. 16 is a diagram illustrating the constitution of whole system of anair-conditioning device according to a second embodiment of the presentinvention;

FIG. 17 is a block diagram of main functions of the second embodiment;

FIG. 18 is a schematic diagram of a neural network of air amountcalculating portion of the second embodiment;

FIGS. 19A, 19B are diagram and graph for explaining an outputcalculating procedure of neural network;

FIG. 20 is a schematic diagram of a neural network of temporary targettemperature calculating portion of the second embodiment;

FIG. 21 is a schematic diagram of a neural network of radiation amountcorrection calculating portion of the second embodiment;

FIG. 22 is a schematic diagram of a neural network of blow-out port modecalculating portion of the second embodiment;

FIG. 23 is a flow chart illustrating a control flow according to thesecond embodiment;

FIG. 24 is a diagram of characteristics illustrating a calculation ofthe blow-out port mode according to the second embodiment;

FIG. 25 is a diagram of characteristic illustrating a relationshipbetween a switching of blow-out port mode and a blower voltage (airamount);

FIGS. 26-29 are diagrams of characteristic illustrating relationshipsbetween the blower voltage (air amount) and a temperature difference TDjust after starting the air-conditioning in summer;

FIG. 30 is a diagram illustrating the constitution of whole system of anair-conditioning device according to a third embodiment of the presentinvention;

FIG. 31 is a block diagram of main functions of the third embodiment;

FIG. 32 is a schematic diagram of a neural network of air amountcalculating portion of the third embodiment;

FIG. 33 is a schematic diagram of a neural network of temporary targettemperature calculating portion of the third embodiment;

FIG. 34 is a schematic diagram of a neural network of radiation amountcorrection calculating portion of the third embodiment;

FIG. 35 is a schematic diagram of a neural network of blow-out port modecalculating portion of the third embodiment;

FIG. 36 is a schematic diagram of an another neural network of blow-outport mode calculating portion of the third embodiment;

FIG. 37 is a diagram illustrating the constitution of whole system of anair-conditioning device according to a fourth embodiment of the presentinvention;

FIG. 38 is a block diagram of main functions of the fourth embodiment;

FIG. 39 is a schematic diagram of a neural network of blow-out port modecalculating portion of the fourth embodiment;

FIGS. 40A, 40B are diagram and graph for explaining an outputcalculating procedure of neural network;

FIG. 41 is a schematic diagram of a neural network of temporary targettemperature calculating portion of the fourth embodiment;

FIG. 42 is a schematic diagram of a neural network of radiation amountcorrection calculating portion of the fourth embodiment;

FIG. 43 is a schematic diagram of a neural network of air amountcalculating portion of the fourth embodiment;

FIG. 44 is a flow chart illustrating a control flow according to thefourth embodiment;

FIG. 45 is a diagram of characteristics illustrating a calculation ofthe blow-out port mode according to the fourth embodiment;

FIGS. 46A-46C and 47A-47B are diagrams illustrating a switchingcharacteristic of a blow-out port mode according to a related art;

FIG. 47C is a diagram illustrating a switching characteristic of ablow-out port mode according to fourth embodiment;

FIG. 48 is a diagram of characteristic illustrating a relationshipbetween input conditions and a blow-out port mode signal output;

FIG. 49 is a diagram of characteristic illustrating a relationshipbetween a comfort in a B/L mode and an external air temperature and atemperature of hot water;

FIG. 50 is a diagram of characteristic illustrating a relationshipbetween input conditions and a blow-out port mode signal output;

FIG. 51 is a schematic diagram of a neural network of blow-out port modecalculating portion of a fifth embodiment;

FIG. 52 is a schematic diagram of a neural network of blow-out port modecalculating portion of a sixth embodiment;

FIG. 53 is a diagram of characteristic illustrating a relationshipbetween input a blow-out port mode and a target blow-out temperatureaccording to a seventh embodiment;

FIG. 54 is a diagram illustrating the constitution of whole system of anair-conditioning device according to a eighth embodiment of the presentinvention;

FIG. 55 is a block diagram of main functions of the eighth embodiment;

FIG. 56 is a schematic diagram of a neural network of blow-out port modecalculating portion of the eighth embodiment;

FIG. 57 is a diagram illustrating the constitution of whole system of anair-conditioning device according to a ninth embodiment of the presentinvention;

FIG. 58 is a schematic diagram of a neural network of a radiationcorrection coefficient calculating portion of the ninth embodiment;

FIGS. 59A, 59B are diagram and graph for explaining an outputcalculating procedure of neural network;

FIG. 60 is a flow chart illustrating a control flow according to theninth embodiment;

FIG. 61 is a diagram of characteristics illustrating a calculation of ablower voltage according to the ninth embodiment;

FIG. 62 is a diagram of characteristics illustrating a calculation of asuction port mode according to the ninth embodiment;

FIG. 63 is a diagram of characteristics illustrating a calculation of ablow-out port mode according to the ninth embodiment;

FIG. 64 is a diagram of characteristics illustrating a calculation of aradiation correction coefficient according to the ninth embodiment;

FIGS. 65A, 65B are diagrams of characteristics illustrating calculationsof a radiation correction coefficient according to the tenth embodiment;

FIG. 66 is a diagram of characteristics illustrating a calculation of aradiation correction coefficient according to the eleventh embodiment;

FIG. 67 is a schematic diagram of a neural network of a radiationcorrection coefficient calculating portion of the eleventh embodiment;

FIG. 68 is a diagram of characteristic illustrating a relationshipbetween input conditions and a radiation correction coefficient outputof the eleventh embodiment;

FIG. 69 is a schematic diagram of a neural network of a radiationcorrection coefficient calculating portion of the twelfth embodiment;

FIG. 70 is a diagram illustrating the constitution of whole system of anair-conditioning device according to a thirteenth embodiment of thepresent invention;

FIG. 71 is a schematic diagram of a neural network of a radiationcorrection coefficient calculating portion of the thirteenth embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

(First Embodiment)

A first embodiment of the present invention will be describedhereinafter with reference to FIGS. 1-11B. FIG. 1 is a diagramillustrating the constitution of whole system of an air-conditioningdevice for automobiles (vehicles), which is capable of controllingindependently between a driver seat (Dr) side air-conditioning zone anda passenger seat (Pa) side air-conditioning zone in a passengercomponent. In FIG. 1, an internal air/external air change-over door 1001is arranged at the most upstream side of air flow in theair-conditioning device. One of an external air and an internal air isselectively introduced into an air duct 1002 by moving the door 1001.

A blower 1003, an evaporator 1004 and a heater core 1005 are arrangedfrom upstream side to downstream side in the air duct 1002. Theevaporator 1004 is a cooling heat exchanger for cooling an air byabsorbing an evaporating latent heat of a refrigerant in a refrigeratingcycle from air. The heater core 1005 is a heating heat exchanger forheating air with heat from a hot water (engine coolant) from a vehicleengine (not shown).

A partitioning wall 1006 is arranged from the heater core 1005 portionto its downstream side in the air duct 1002 to divide the air duct 1002into a Dr side air passage 1007 and a Pa side air passage 1008.

A Dr side air-mixing door 1009 is arranged at the upstream of the heatercore 1005 in the Dr side air passage 1007. The Dr side air-mixing door1009 adjusts a ratio of two air amount in the Dr side air passage 1007,an amount of heated air passed through the heater core 1005 and anamount of cooled air by-passed the heater core 1005. A Pa sideair-mixing door 1010 is arranged at the upstream of the heater core 1005in the Pa side air passage 1008, and adjusts a ratio of two air amountin the Pa side air passage 1008. Here, the two air amount are an amountof heated air passed through the heater core 1005 and an amount ofcooled air by-passed the heater core 1005.

At the most downstream side of the Dr side air passage 1007 and the Paside air passage 1008, foot blow-out ports 1007 a, 1008 a, face blow-outports 1007 b, 1007 c, 1008 b, 1008 c, and a defroster blow-out port 11are provided. Here, the foot blow-out ports 1007 a, 1008 a are providedto blow the conditioned air onto the feet of the passengers. The faceblow-out ports 1007 b, 1007 c, 1008 b, 1008 c are provided at each ofcenter portion and side portion in the passenger compartment to blow theconditioned air to the upper half of the body of the passengers. Thedefroster blow-out port 1011 is provided to blow the conditioned air toa windshield.

In the Dr side air passage 1007 and the Pa side air passage 1008,blow-out port change-over doors 1012-1016 are arranged to selectivelyopen/close the blow-out ports 1007 a-1007 c, 1008 a-1008 c and 1011.Each of predetermined blow-out modes, such as a FACE mode (FACE), aBI-LEVEL mode (B/L mode), and a FOOT mode (FOOT) or the like can be setwith respective to each ports 1007, 1008, independently, by changing theopen/close condition of the doors 1012-1016.

Here, a control system for controlling the air-conditioning device willbe explained. The internal air/external air changing door 1001, the Drside air-mixing door 1009, the Pa side air-mixing door 1010, and theblow-out port change-over doors are driven by servomotors 1017-1022. Theservomotors 1017-1022 are controlled by outputs of an air-conditioningelectric control device 1023 (hereinafter, called “ECU 1023”). A motor1003 a of the blower 1003 is also controlled by the output of the ECU1023 via a control circuit (motor applied voltage control circuit) 1024.The ECU 1023 includes a microcomputer and its peripheral circuits.

A Dr side temperature setter (first temperature setter) 1025 is providedto set a Dr side setpoint temperature Tset(Dr) of the Dr sideair-conditioning zone relative to the Dr side air passage 1007, andoutput the setpoint temperature Tset(Dr) to the ECU 1023. A Pa sidetemperature setter (second temperature setter) 1026 is provided to set aPa side setpoint temperature Tset(Pa) of the Pa side air-conditioningzone relative to the Pa side air passage 1008, and output the setpointtemperature Tset(Pa) to the ECU 1023. Both temperature setters 1025,1026 are provided independently of each other.

As temperature data detectors, an internal air sensor 1027 is arrangedto detect an internal air temperature Tr. An external air sensor 1028 isarranged to detect an external air temperature Tam. An evaporatortemperature sensor 1029 is arranged to detect a cooling temperature(blow-out air temperature) of the evaporator 1004. A water temperaturesensor 1030 is arranged to detect a temperature Tw of hot water flowinginto the heater core 1005.

Furthermore, a Dr side radiation sensor 1031 is arranged to detect a(solar) radiation amount TsDr to the Dr side air-conditioning zone, anda Pa side radiation sensor 1032 is arranged to detect a (solar)radiation amount TsPa to the Pa side air-conditioning zone.

Control functions processed by the microcomputer in the ECU 1023 aregenerally divided as shown in FIG. 2. The ECU 1023 includes first andsecond temporary target temperature calculating portions 1033, 1034,first and second radiation amount correction calculating portions 1035,1036, first and second blow-out port mode calculating portions 1037,1038, first and second air amount calculating portion 1039, 1040, firstand second target temperature calculating portions 1041, 1042, an airamount calculating portion 1043, a Dr side air-mixing door openingdegree calculating portion 1044, a Pa side air-mixing door openingdegree calculating portion 1045.

Here, the first temporary target temperature calculating portion 1033calculates a Dr side temporary target blow-out temperature TAOB(Dr). Thesecond temporary target temperature calculating portion 1034 calculatesa Pa side temporary target blow-out temperature TAOB(Pa).

The first temporary target temperature calculating portion 1033 includesa neural network 1100 as shown FIG. 3, and inputs the signals includingthe internal air temperature Tr, the external air temperature Tam,setpoint temperature Tset(Dr) from the Dr side temperature setter 1025and a setpoint temperature difference Δ TsetDr, which is a difference ofthe both setpoint temperatures Tset(Dr) and Tset(Pa). And the firsttemporary target temperature calculating portion 1033 calculates the Drside temporary target blow-out temperature TAOB(Dr) based on the inputsignals via the neural network 1100.

Similarly, the second temporary target temperature calculating portion1034 includes a neural network 1100 as shown FIG. 3, inputs the signalsincluding the internal air temperature Tr, the external air temperatureTam, setpoint temperature Tset(Pa) from the Pa side temperature setter1026, and a setpoint temperature difference ΔTsetPa, which is adifference of the both setpoint temperatures Tset(Dr) and Tset(Pa). Thesecond temporary target temperature calculating portion 1034 calculatesthe Pa side temporary target blow-out temperature TAOB(Pa) based on theinput signals via the neural network 1100.

The first radiation amount correction calculating portion 1035calculates a Dr side radiation amount correction TAOS(Dr). The secondradiation amount correction calculating portion 1036 calculates a Paside radiation amount correction TAOS(Pa).

The first blow-out port mode calculating portion 1037 calculates a Drside blow-out port mode signal TMODE(Dr). The second blow-out port modecalculating portion 1038 calculates a Pa side blow-out port mode signalTMODE(Pa).

Furthermore, the first air amount calculating portion 1039 calculates ablower voltage level TBLO(Dr) which decides a Dr side air amount. Thesecond air amount calculating portion 1040 calculates a blower voltagelevel TBLO(Pa) which decides a Pa side air amount.

The first and the second radiation amount correction calculatingportions 1035, 1036 include a neural network 1200 as shown in FIG. 5.The first and the second blow-out port mode calculating portions 1037,1038 include a neural network 1300 as shown in FIG. 7. The first and thesecond air amount calculating portions 1039, 1040 include a neuralnetwork 1400 as shown in FIG. 8.

The first target temperature calculating portion 1041 calculates a Drside final target blow-out temperature TAO(Dr) based on the output ofthe first temporary target temperature calculating portion 1033 and theoutput of the first radiation amount correction calculating portion1035. The second target temperature calculating portion 1042 is providedto calculate a Pa side final target blow-out temperature TAO(Pa) basedon the output of the second temporary target temperature calculatingportion 1034 and the output of the second radiation amount correctioncalculating portion 1036.

The air amount calculating portion 1043 calculates blower voltage levelTBLO corresponding to the final amount on air, based on an average ofthe blower voltage levels TBLO(Dr), TBLO(Pa), which are calculated bythe first and second air amount calculating portions 1039, 1040.

The Dr side air-mixing door opening degree calculating portion 1044calculates a Dr side air-mixing door opening degree SW(Dr) based on theTAO(Dr) output from the first target temperature calculating portion1041, and a Pa side air-mixing door opening degree calculating portion1045 to calculate a Pa side air-mixing door opening degree SW(Pa) basedon the TAO(Pa) output from the second target temperature calculatingportion 1042.

The constitutions of the neural networks 1100-1400 will be explained.Since the constitutions of the neural networks 1100-1400 aresubstantially equal, these constitutions are explained based on theneural network 1100 with reference to FIGS. 3, 4A, 4B.

As shown in FIG. 3, the neural network 1100 includes an input layer1101, a first intermediate layer 1102, a second intermediate layer 1103,and output layer 1104. Each of the input layer 1101, the first and thesecond intermediate layers 1102, 1103 has a plurality of neurons 1105,and output layer has one neuron 1105. Each of neurons 1105 in the inputlayer 1101 is connected to each of neurons 1105 in the firstintermediate layer 1102 with a certain link coefficient (synapse weight)1106. Similarly, each of neurons 1105 in the first intermediate layer1102 is connected to each of neurons 1105 in the second intermediatelayer 1103 with a certain link coefficient 1106. Each of neurons 1105 inthe second intermediate layer 1103 is connected to the neuron 1105 inthe output layer 1104 with a certain link coefficient 1106. Theseneurons 1105 are formed by memories in the ECU 1023 in an actualcircuit, and the neural network 1100 is formed by combined memories.

The neural network 1100, which is a network having a layered structure,has a Back Propagation Learning Function that can automatically correctthe link coefficients 1106 between each neurons among the input layer1101, the first and second intermediate layers 1102, 1103, and outputlayer, so as to adjust its output to a desired value (teacher signal)related to input signals such as Tseti, Tr, Tam, ΔTseti.

When the teacher signal is changed, the link coefficients 1106 areadjusted by repeatedly “learning” so as to correct the output to be thechanged teacher signal related to the input signals. In other words,based on a large number of desired data (teacher signals), a correlationfunction (link coefficients 1106) is automatically generated. Theteacher signals are determined to desired values obtained by anexperience or the like (desired output for the input signals).

In the layered structure neural network 1100, there is no connectionamong each of the neurons 1105 in an identical layer, but the neurons1105 in each layer are connected to only neurons 1105 in forward andbackward layers. The link coefficients 1106 between each neuron 1105 ineach layer represent weights of connections (magnitude). The larger theweight of connection, the larger an amplitude of signal, which is outputfrom each neuron 1105 and propagated to neurons 1105 in backward layer.

Here, the input signals (Tseti, Tr, Tam, Δtseti) are normalized to 0-1value before inputting to the neural network 1100 by a normalize portion1107. The output signal from the neural network 1100 is transformed inreverse procedure of the normalize portion 1107 by an output transformportion 1108. For example, actual detected internal temperature Trdetected by the internal air sensor is normally in a range from 0° C. to50° C. This detected value is assigned to a certain value in a rangefrom 0 to 1 in the normalize portion 1107, and is output to the inputlayer 1101 in the neural network 1100. Since the output signal from theoutput layer 1104 is a value in the range from 0 to 1, this outputsignal is transformed to the actual value corresponding to the sensorsignal in the reverse procedure by using a predetermined transformationmap in the output transform portion 1108.

The environment conditions, of which the air-conditioning device forvehicle faces, are variable, as described above. Therefore, the numberof the teacher signals, which are desired values related to the variousenvironment conditions, will be enormously.

Therefore, in a designing process of the ECU 1023, the learning isexecuted by a high-speed calculator including a neural network, which isthe same as the neural network 1100, before being mounted to thevehicle. The high-speed calculator calculates the link coefficients 1106so that the output of the neural network is to be desired teachersignals for various input signals. Then, the calculated coefficients1106 are memorized in a memory portion (ROM) corresponding to eachneural network 1100, 1200, 1300, 1400 in each of the calculatingportions of the ECU 1023 shown in FIG. 2.

Therefore, when the ECU 1023 is mounted on the vehicle, the linkcoefficients 1106 among the neurons 1105 in each layer of the neuralnetworks 1100-1400 have been set to predetermined value.

After being mounted on the vehicle, the neural networks 1100-1400 ineach layer calculate an output for input signals as shown in FIG. 4A.Specifically, in each of the neurons 1105, each of the input signalsO₁-O_(n) is multiplied by each of the corresponding coefficients 1106(W₁-W_(n)), and multiplied values are adapted to a so-called sigmoidfunction as shown FIG. 4B. The calculated results of the sigmoidfunction are output to the neurons 1105 in backward layer. Thesecalculations are repeated between each layer to decide a final output.

Since the neural network 1100 in each calculating portion of mounted ECU1023 is previously set so that the desired value for the various inputsignals O₁-O_(n) can be obtained, learning for correction of the linkcoefficients is not needed.

Therefore, the neural network 1100 of the first and second temporarytarget temperature calculating portions 1033, 1034 output the desiredteacher signals as the temporary target blow-out temperature TAOBirelated to the change of the input signals (Tseti, Tr, Tam, Δtseti).Here, a subscript “i” represents either “Dr” or “Pa”, and will be usedhereinafter as the same meaning.

The Dr side temporary target blow-out temperature TAOB(Dr) calculated bythe neural network 1100 of the first temporary target temperaturecalculating portions 1033 is input to the first target temperaturecalculating portion 1041 which calculates the Dr side final targetblow-out temperature TAO(Dr). The Pa side temporary target blow-outtemperature TAOB(Pa) calculated by the neural network 1100 of the secondtemporary target temperature calculating portions 1034 is input to thefirst target temperature calculating portion 1042 which calculates thePa side final target blow-out temperature TAO(Pa).

The neural network 1200 of the first and second radiation amountcorrection calculating portions 1035, 1036 output the desired teachersignals as the radiation amount correction TAOSi relate to the change ofthe input signals (Tseti, Tr, Tam, Δtseti). Next, a calculationprocedure of the radiation amount correction TAOSi will be explainedwith reference to FIGS. 6A-6C. In FIGS. 6A-6C, the abscissa representsthe internal air temperature Tr.

FIG. 6A shows that the radiation amount correction TAOSi is set to zero,independent of the setpoint temperature Tseti, the external airtemperature Tam, and the water temperature Tw, when the radiation amountTsi=0.

FIG. 6B shows a characteristic of the radiation amount correction TAOSiwhen the radiation amount Tsi=1 kW/m², the setpoint temperatureTseti=25° C., the external air temperature Tam=<−10° C. The radiationamount correction TAOSi is risen from TAOSi=0 at the point the internalair temperature Tr=T3, and is saturated with Ta1 at the point theinternal air temperature Tr=T4, independent of the water temperature Tw,

Further, FIG. 6C shows the characteristic of the radiation amountcorrection TAOSi when the radiation amount Tsi=1 kW/m², the setpointtemperature Tseti=25° C., the external air temperature Tam=>10° C. Theradiation amount correction TAOSi is risen from TAOSi=0 at the point theinternal air temperature Tr=T1. Then, it is saturated with Ta1 at thepoint the internal air temperature Tr=T2, independent of the watertemperature Tw, Here, the internal air temperature has a relationship ofT1<T2<T3<T4.

When the external air temperature Tam is within a range of −10° C. and10° C., the internal air temperature Tr at the time the radiation amountcorrection TAOSi rises is set to intermediate temperature between T1 andT3, and the internal air temperature Tr at the time the radiation amountcorrection TAOSi saturates is set to intermediate temperature between T2and T4.

According to the characteristic of the radiation amount correction TAOSishown in FIGS. 6A-6C, when the internal air temperature Tr is in a lowtemperature field (warm-up control field when a heating is started inwinter), the internal air temperature is risen mainly by the radiationwithout the radiation correction. On the other hand, when the internalair temperature Tr is in a high temperature field which is higher that agiven temperature, the radiation correction is executed so that thevariation on internal air temperature control of the passenger componentdue to the radiation can be restricted.

In the first and the second target temperature calculating portions1041, 1042, the final target blow-out temperatures TAOi are calculatedby the following equation:

TAOi=TAOBi−TAOS  (1)

In the Dr side air-mixing door opening degree calculating portion 1044and the Pa side air-mixing door opening degree calculating portion 1045,the Dr side air-mixing door opening degree SW(Dr) and the Pa sideair-mixing door opening degree SW(Pa) are calculated based on theTAO(Dr) and TAO(Pa), which are output from the first and the secondtarget temperature calculating portion 1041, 1042, the temperature Te ofthe evaporator 1004, and the water temperature Tw of the heater core1005, by using the following equation:

Swi(%)=(TAOi−Te)/(Tw−Te)×100  (2)

The neural networks 1300 as shown in FIG. 7, which constitutes either ofthe first and the second blow-out port mode calculating portions 1037,1038, calculate and output the desired value as the blow-out port modesignal TMODE(Dr) on the Dr side and the blow-out port mode signalTMODE(Pa) on the Pa side, respectively, in relation to the changes ofinput signals (TAOi, Tsi, Tam, Tw).

The neural networks 1400 as shown in FIG. 8, which constitutes either ofthe first and the second air amount calculating portions 1039, 1040,calculate and output the desired value as the Dr side blower voltagelevel TBLO(Dr), the Pa side blower voltage level TBLO(Pa), respectively,in relation to the changes of input signals (Tseti, Tr, Tam, Tsi).

The control flow according to the preferred embodiment will be explainedwith reference to a flow chart shown in FIG. 9. This control routinestarts from a control switch signal, which starts the air-conditioningdevice.

At step 1510, the air-conditioning ECU 1023 is reset. At step 1520, theECU 1023 inputs the output signals from each sensors such as theinternal air temperature Tr, the external air temperature Tam, and thesignals from the Dr side temperature setter 1025 and the Pa sidetemperature setter 1026.

At step 1530, the temporary target blow-out temperatures TAOBi arecalculated via the neural network 1100, shown in FIG. 3. The step 1530corresponds to the first and the second temporary target temperaturecalculating portions 1033, 1034 in FIG. 2.

At step 1540, the amounts of radiation corrections TAOSi are calculatedvia the neural network 1200, shown in FIG. 5. The step 1540 correspondsto the first and the second radiation amount correction calculatingportions 1035, 1036 in FIG. 2.

At step 1550, the final target blow-out temperatures TAOi are calculatedby the equation (1). The step 1550 corresponds to the first and thesecond target temperature calculating portions 1041, 42.

At step 1560, the air-mixing door opening degrees Swi are calculated bythe equation (2). The step 1560 corresponds to the Dr side air-mixingdoor opening degree calculating portion 1044 and the Pa side air-mixingdoor opening degree calculating portion 1045.

At step 1570, the blow-out port mode signals TMODEi are calculated viathe neural network 1300, shown in FIG. 7. The step 1570 corresponds tothe first and the second blow-out port mode calculating portions 1037,1038. Here, outputs in the blow-out port mode signals TMODEi havecharacteristics that these outputs increase in proportion to anincreasing of the target blow-out temperature TAOi. One of the FACE mode(FACE), the B/L mode, and the FOOT mode (FOOT), which are shown in FIG.10, is determined based on the output TMODEi from the neural network1300.

At step 1580, the Dr side blower voltage level TBLO(Dr) and the Pa sideblower voltage level TBLO(Pa) are calculated via the neural network1400, shown in FIG. 8. Then, both of the blower voltage levels TBLO(Dr),TBLO(Pa) are averaged to obtain final blower voltage levels TBLO. Thestep 1580 corresponds to the air amount calculating portions 1039, 1040,and 1043.

At steps 1590-1610, each of actuators such as motors 1017-1022 and themotor applied voltage control circuit 1024 as shown in FIG. 1 arecontrolled so as to correspond to the above-mentioned calculated values.

Next, merits of this preferred embodiment, in which the target blow-outtemperatures TAOi are calculated via the neural networks 1100, 1200,will be explained in detail.

FIG. 11C is a diagram illustrating a relationship between input signals1-4 and output signal (teacher signal) of neural network 1100. When theinput signals 1-4 are 28° C., 25° C., 10° C., 3° C., and the outputsignal is 50° C., the output signal is changed from 50° C. to 35° C. incase the disadvantage (temperature of area surrounding passengers in Paside is 25.5° C.), shown in FIG. 12, occurs.

Here, the neural network has the learning function, which adjusts thelink coefficients (synapse weights) 1106 between each neurons in eachlayers in the neural network automatically to correct its output to bedesired data (teacher signal). Therefore, the output at a specific inputcondition can be adjusted, by changing the teacher signal at thespecific input condition and then adjusting the link coefficients 1106automatically in advance.

By above-mentioned changing of output (change from 50° C. to 35° C.),the temperature of area surrounding passengers in Pa side can becontrolled to substantially 28° C. as set, when the Pa side setpointtemperature Tset(Pa)=28° C.

Furthermore, since the neural network adjusts its whole linkcoefficients 1106 so that the desired outputs (teacher signal) at theother input condition are maintained even if the output at the specificinput condition is changed. Therefore, the output change at the specificinput condition does not influence the outputs at the other inputconditions.

As shown in FIGS. 11A, 11B, when both the Dr side and Pa side setpointtemperatures are changed, both temperatures of the area surroundingpassengers in the Dr side and Pa side are highly independent controlledwith accuracy, without the temperature interference between each sides.

Furthermore, since the correction of the link coefficients 1106 areadjusted automatically by using the learning function of the neuralnetwork, it will not be essential for the engineers to construct acomplicated control logic such that the desired output can be obtainedonly at specific input condition. Hence, engineer's process can beeliminated substantially.

Since the complicated process is not needed, a capacitance of a memory(ROM) of a computer, which constitutes an air-conditioning electriccontrol device, can be decreased.

Furthermore, the temporary target blow-out temperatures TAOBi arecalculated via the neural network 1100 shown in FIG. 3. The amounts ofradiation corrections TAOSi are calculated via the neural network 1200shown in FIG. 5. The final target blow-out temperatures TAOi arecalculated by the equation (1) based on the TAOBi and TAOi. Therefore,since the radiation amount signal Tsi can be eliminated from the inputsignals, the number of input signals to the neural network 1100 can bedecreased. Then, the number of intermediate layers can be decreased, andthe layered structure of the neural network 1100 can be simplified.

Now, it is acceptable to combine above-mentioned two neural networks1100, 1200 to one neural network to calculate the final target blow-outtemperature TAOi directly by the combined neural network, in place ofthe independent two neural networks 1100, 1200.

Here, the setpoint temperatures (Tset(Dr), Tset(Pa)) of each the firstand the second air-conditioning zones, which are input to the first andthe second target blow-out temperature calculating portions, require atleast one of the setpoint temperatures Tset(Dr) or Tset(Pa) of eachair-conditioning zones relative to each calculating portions. It isacceptable for this device to input a temperature difference ΔTsetibetween the setpoint temperatures Tset(Dr) and Tset(Pa) in addition tothe selected the setpoint temperature, or to input both of the setpointtemperatures Tset(Dr) and Tset(Pa).

In other words, at least one of the setpoint temperatures Tset(Dr) orTset(Pa) of each air-conditioning zones relative to each calculatingportions is input. And further a signal, of which reflects an influencefrom the setpoint temperature of the opposite side air-conditioningzone, is input to calculate the target blow-out temperature.

The first embodiment can be also modified as follows.

(1) The neural network 1100 inputs the ΔTseti, which is temperaturedifference between the setpoint temperatures Tset(Dr) and Tset(Pa), asone of input signals when the temporary target blow-out temperaturesTAOBi are calculated. However, when the Dr side temporary targetblow-out temperature TAOB(Dr) is calculated, it is acceptable to inputthe setpoint temperatures Tset(Dr) and Tset(Pa) instead of the ΔTseti.Also, when the Pa side temporary target blow-out temperature TAOB(Pa) iscalculated, it is acceptable to input the setpoint temperatures Tset(Pa)and Tset(Dr) instead of the ΔTseti. This way can also control bothtemperatures of the area surrounding passengers in the Dr side and Paside independently and accurately.

(2) In the first embodiment, the blow-out modes of each the Dr side airpassage 1007 and the Pa side air passage 1008 are determinedindependently each other. However, it is acceptable to determine theblow-out modes of each the Dr side air passage 1007 and the Pa side airpassage 1008 to identical blow-out mode. In this case, a TAOX, which isan average of the first and the second target blow-out temperaturesTAO(Dr) and TAO(Pa), and a TsX, which is an average of the radiationamount TsDr to the Dr side air-conditioning zone and the radiationamount TsPa to the Pa side air-conditioning zone, are input as the inputto the neural network 1300 to calculate the blow-out port mode signalTMODE.

(3) In the first embodiment, the blow-out port mode and air amount(blower voltage level) are calculated via the neural networks 1300,1400. However, it is acceptable to calculate the blow-out port mode andair amount (blower voltage level) by conventional calculation based onthe TAOi without neural networks.

(4) The learning method of the neural network can be replaceable withSemi Newton method or the like instead of the above-mentioned backpropagation method.

(5) Several of calculations for air-conditioning control are executedvia the neural networks 1100-1400. However, it is replaceable with fuzzycontrol system instead of the neural networks 1100-1400.

That is, in the fuzzy system, both a membership function and a fuzzyrule table are changed. By this changing, the desired outputs at theother input condition are maintained even if the output at the specificinput condition is changed, and the output change at the specific inputcondition does not influence the outputs at the other input conditions.Therefore, it can also control both temperatures of the area surroundingpassengers in the Dr side and Pa side (shown in FIGS. 11A, 11B)independently and accurately as well as the air-conditioning deviceusing above-mentioned neural networks.

Here, the air-conditioning device using above-mentioned neural networksis superior to the air-conditioning device using the fuzzy in the pointof view of total process of the engineers.

(6) In the first embodiment, the air-mixing doors 1009, 1010, which arethe temperature adjuster to adjust the blow-out air temperature to eachDr side air-conditioning zone and the Pa side air-conditioning zone in apassenger component independently, are arranged in the Dr side airpassage 1007 and the Pa side air passage 1008 independently. However, itis replaceable with the following structure. That is, the heater cores1005 are independently arranged in the Dr side air passage 1007 and thePa side air passage 1008. A Dr side hot water valve is arranged tocontrol amount of hot water flowing into the heater core 1005 of the Drside air passage 1007 or control a temperature on the hot water. A Paside hot water valve is arranged to control amount of hot water flowinginto the heater core 1005 of the Pa side air passage 1008 or control atemperature on the hot water. Then, the blow-out air temperature to eachDr side air-conditioning zone and the Pa side air-conditioning zone in apassenger component are adjusted independently by the two hot watervalves.

(7) The present invention can be adaptable to another air-conditioningdevice, which has plurality of air-conditioning zone, and is needed tocontrol each of temperature of blow-out air to each zones independently.

(8) The neural networks 1100-1400 are replaceable with recurrent typeneural networks.

(Second Embodiment)

A second embodiment of the present invention will be describedhereinafter with reference to FIGS. 16-24 and 26-29. FIG. 16 is adiagram illustrating the constitution of whole system of anair-conditioning device for automobiles. In FIG. 16, an internalair/external air change-over door 2001 is arranged at the most upstreamside of air flow in the air-conditioning device for automobiles. One ofan external air and an internal air is selectively introduced into anair duct 2002 by moving the door 2001.

The air duct 2002 constitutes an air passage of the air-conditioningdevice. A blower 2003, an evaporator 2004 and a heater core 2005 arearranged from upstream side to downstream side in the air duct 2002. Theevaporator 2004 is a cooling heat exchanger for cooling an air byabsorbing an evaporating latent heat of a refrigerant in a refrigeratingcycle from air. The heater core 2005 is a heating heat exchanger forheating air with heat from a hot water (engine coolant) from a vehicleengine (not shown).

An air-mixing door 2006 as a temperature adjuster is arranged at theupstream of the heater core 2005. The air-mixing door 2006 adjusts aratio of two air amount, wherein an amount of heated air passed throughthe heater core 2005 and an amount of cooled air by-passed the heatercore 2005. The adjusting the ratio of heated air and cooled air canadjusts the blow-out air temperature to a passenger component of theautomobile.

At the most downstream side of the air duct 2, foot blow-out ports 2008a, 2008 b, face blow-out ports 2009 a-2009 d, and a defroster blow-outport 2010 are provided. Here, the foot blow-out ports 2008 a, 2008 b areprovided to blow the conditioned air onto the feet of the passengers.The face blow-out ports 2009 a-2009 d are provided at each of centerportion and side portion in the passenger component to blow theconditioned air to the upper half of the body of the passengers. Thedefroster blow-out port 2010 is provided to blow the conditioned air toa windshield.

At the most downstream side of the air duct 2002, blow-out portchange-over doors 2011-2013 are arranged to selectively open/close theblow-out ports 2008 a, 2008 b and 2009 a-2009 d. Each of predeterminedblow-out modes, such a FACE mode (FACE), a BI-LEVEL mode (B/L mode), aFOOT mode (FOOT), and a defroster mode or the like can be set bychanging the open/close condition of the doors 2011-2013.

Here, a control system for controlling the air-conditioning device willbe explained. The internal air/external air changing door 2001, theair-mixing door 2006, and the blow-out port change-over doors are drivenby servomotors 2014-2018. The servomotors 2014-2018 are controlled byoutputs of an air-conditioning electric control device 2019(hereinafter, called “ECU 2019”). A motor 2003 a of the blower 2003 isalso controlled by the output of the ECU 2019 via a motor controlcircuit (motor applied voltage control circuit) 2020.

An air amount blown by the blower 2003 is adjusted by the motor controlcircuit 2020 by way of changing a rotation speed of the motor with anapplied voltage to the motor. The ECU 2019 includes a microcomputer andits peripheral circuits.

A temperature setter 2021 is provided to set a setpoint temperature Tsetof the passenger component, which is input to the ECU 2019. Thetemperature setter 2021 is arranged an air-conditioning control panel2027, and is manually controlled by users.

As temperature data detectors, the following sensors are provided. Thatis, an internal air sensor 2022 is provided to detect an internal airtemperature Tr. An external air sensor 2023 is provided to detect anexternal air temperature Tam. A radiation sensor 2024 is provided todetect an amount of (solar) radiation Ts to the passenger component. Anevaporator temperature sensor 2025 is provided to detect a coolingtemperature Te (blow-out air temperature) of the evaporator 2004. Awater temperature sensor 2026 is provided to detect a water temperatureTw of hot water flowing into the heater core 2005.

Control functions processed by the microcomputer in the ECU 2019 isgenerally divided as shown in FIG. 17. The ECU 2019 includes a temporarytarget temperature calculating portion 2028, a radiation amountcorrection calculating portion 2029, a target temperature calculatingportion 2030, an air-mixing door opening degree calculating portion2031, a blow-out port mode calculating portion 2032, an air amountcalculating portion 2033, and an air amount selecting portion 2034.

Here, the temporary target temperature calculating portion 2028calculates a temporary target blow-out temperature, and includes aneural network 2100 as shown in FIG. 20. The temporary targettemperature calculating portion 2028 inputs signals including thesetpoint temperature Tset, the internal air temperature Tr and theexternal air temperature Tam, and calculates the temporary targetblow-out temperature TAOB based on the input signals via the neuralnetwork 2200.

The radiation amount correction calculating portion 2029 calculates aradiation amount correction TAOS, and includes a neural network 2300 asshown in FIG. 21. The radiation amount correction calculating portion2029 inputs signals including the setpoint temperature Tset, theinternal air temperature Tr, the external air temperature Tam and theradiation amount Ts (output signal of the radiation sensor 2024). Thenthe radiation amount correction calculating portion 2029 calculates theradiation amount correction TAOS based on the input signals via theneural network 2300.

The target temperature calculating portion 2030 calculates the finaltarget blow-out temperature TAO based on the output signals from thetemporary target temperature calculating portion 2028 and the radiationamount correction calculating portion 2029.

The air-mixing door opening degree calculating portion 2031 calculatesan air-mixing door opening degree SW based on the final target blow-outtemperature TAO from the target temperature calculating portion 2030.

The blow-out port mode calculating portion 2032 calculates a blow-outport mode TMODE, and includes a neural network 2400 as shown in FIG. 22.The blow-out port mode calculating portion 2032 inputs the final targetblow-out temperature TAO from the target temperature calculating portion2030 and the detected information, which is the circumstance factor toinfluence a temperature sensitively of the passenger such as theradiation amount Ts, the external air temperature Tam and the watertemperature Tw. Then the blow-out port mode calculating portion 2032calculates the blow-out port mode TMODE via the neural network 2400.

The air amount calculating portion 2033 includes a neural network 2100as shown in FIG. 18. The air amount calculating portion 2033 inputssignals including a temperature difference TD between the setpointtemperature Tset and the internal air temperature Tr (i.e., Tr−Tset),the radiation amount Ts and the external air temperature Tam. Then theair amount calculating portion 2033 calculates a blower voltage, whichdecides an air amount via the neural network 2100. In detail, the airamount calculating portion 2033 calculates a blower voltage TBLO₁ forthe FACE, B/L modes, and a blower voltage TBLO₂ for the FOOT mode,independently.

The air amount selecting portion 2034 selects one of the blower voltageTBLO₁ and the blower voltage TBLO₂ based on the blow-out port mode, andoutput the selected one as a blower voltage TBLO.

The constitutions of the neural networks 2100-2400 will be explained.Since the constitutions of the neural networks 2100-2400 aresubstantially equal, these constitutions are explained based on theneural network 2100 with reference to FIGS. 18, 19A, 19B.

As shown in FIG. 18, the neural network 2100 includes an input layer2101, a first intermediate layer 2102, a second intermediate layer 2103,and output layer 2104. Each of the input layer 2101, the first and thesecond intermediate layers 2102, 2103 has a plurality of neurons 2105,and output layer has one neuron 2105. Each of neurons 2105 in the inputlayer 2101 is connected to each of neurons 2105 in the firstintermediate layer 2102 with a certain link coefficient (synapse weight)2106. Similarly, each of neurons 2105 in the first intermediate layer2102 is connected to each of neurons 2105 in the second intermediatelayer 2103 with a certain link coefficient 2106. Each of neurons 2105 inthe second intermediate layer 2103 is connected to the neuron 2105 inthe output layer 2104 with a certain link coefficient 2106. Theseneurons 2105 are formed by memories in the ECU 2019 in an actualcircuit, and the neural network 2100 is formed by combined memories.

The neural network 2100, which is a network having a layered structure,has a Back Propagation Learning Function. This learning function canautomatically correct the link coefficients 2106 between each neuronsamong the input layer 2101, the first and second intermediate layers2102, 2103, and output layer, so as to adjust its output to a desiredvalue (teacher signal) related to input signals such as TD, Ts, Tam.

When the teacher signal is changed, the link coefficients 2106 areadjusted by repeatedly “learning” so as to correct the output to be thechanged teacher signal related to the input signals. In other words,based on a large number of desired data (teacher signals), a correlationfunction (link coefficients 2106) is automatically generated. Theteacher signals are determined to desired values obtained by anexperience or the like (desired output for the input signals).

In the layered structure neural network 2100, there is no connectionamong each of the neurons 2105 in an identical layer, but the neurons2105 in each layer are connected to only neurons 2105 in forward andbackward layers. The clink coefficients 2106 between each neuron 2105 ineach layer represent a weight of connection (magnitude). The larger theweight of connection, the larger an amplitude of signal, which is outputfrom each neuron 2105 and propagated to neurons 2105 in backward layer.

Here, the input signals (TD, Ts, Tam) are normalized to 0-1 value beforeinputting to the neural network 2100 by a normalize portion 2107. Theoutput signal from the neural network 2100 is transformed in reverseprocedure of the normalize portion 2107 by output transform portions2108 a, 2108 b. For example, actual detected internal temperature Trdetected by the internal air sensor is normally in a range from 0° C. to50° C. This detected value is assigned to a certain value in a rangefrom 0 to 1 in the normalize portion 2107, and is output to the inputlayer 2101 in the neural network 2100. Since the output signal from theoutput layer 2104 is a value in the range from 0 to 1, this outputsignal is transformed to the actual value corresponding to the sensorsignal in the reverse procedure by using a predetermined transformationmap in the output transform portions 2108 a, 2108 b.

Here, the output layer 2104 of the neural network 2100 for the airamount calculation includes two output neurons 2105 a, 2105 b. Theneural network 2100 outputs the blower voltage TBLO₁ for the FACE, B/Lmodes and the blower voltage TBLO₂ for the FOOT mode, independently, viathe two output transform portions 2108 a, 2108 b.

The environment conditions, of which the air-conditioning device forvehicle faces, are variable, as described above. Therefore, the numberof the teacher signals, which are desired values related to the variousenvironment conditions, will be enormously.

Therefore, in a designing process of the ECU 2019, the learning isexecuted by a high-speed calculator including a neural network, which isthe same as the neural network 2100, before being mounted to thevehicle. The high-speed calculator calculates the link coefficients 2106so that the output of the neural network is to be desired teachersignals for various input signals. Then, the calculated coefficients2106 are memorized in a memory portion (ROM) corresponding to eachneural network 2100, 2200, 2300, 2400 in each of the calculatingportions of the ECU 2019 shown in FIG. 17.

Therefore, when the ECU 2019 is mounted on the vehicle, the linkcoefficients 2106 among the neurons 2105 in each layer of the neuralnetworks 2100-2400 are set to predetermined value.

After being mounted on the vehicle, the neural networks 2100-2400 ineach layer calculate an output for input signals as shown in FIG. 19A.Specifically, in each of the neurons 2105, each of the input signalsO₁-O_(n) is multiplied by each of the corresponding coefficients 2106(W₁-W_(n)), and multiplied value are adapted to a so-called sigmoidfunction as shown FIG. 19B. The calculated results of the sigmoidfunction are output to the neurons 2105 in backward layer. Thesecalculations are repeated between each layer to decide a final output.

Since the neural network 2100 in each calculating portion of mounted ECU2019 is previously set so that the desired value for the various inputsignals O₁-O_(n) on can be obtained, learning for correction of the linkcoefficients is not needed.

The neural networks 2100 as shown in FIG. 18 of the air amountcalculating portion 2033 calculates and outputs the desired value as theblower voltage levels TBLO₁, TBLO₂, in relation to the changes of inputsignals (TD, Ts, Tam).

The neural network 2200 of the temporary target temperature calculatingportion 2028 outputs the desired teacher signals as the temporary targetblow-out temperature TAOB in relation to the change of the input signals(Tseti, Tr, Tam).

The neural network 2300 of the radiation correction calculating portion2029 outputs the desired teacher signals as the radiation amountcorrection TAOS in relation to the change of the input signals (Tset,Tr, Tam, Ts).

The neural networks 2400 as shown in FIG. 22 of the blow-out port modecalculating portion 2032 calculates and outputs the desired value as theblow-out port mode signal TMODE in relation to the changes of inputsignals (TAO, Ts, Tam, Tw).

In the target temperature calculating portion 2030, the final targetblow-out temperature TAO is calculated by the following equation:

TAO=TAOB−TAOS  (3)

In the air-mixing door opening degree calculating portion 2031, theair-mixing door opening degree SW is calculated based on the TAO fromthe target temperature calculating portion 2030, the temperature Te ofthe evaporator 2004, and the water temperature Tw of the heater core2005, by using the following equation:

Sw(%)=(TAO−Te)/(Tw−Te)×100  (4)

The control flow according to the preferred embodiment will be explainedwith reference to a flow chart shown in FIG. 23. This control routinestarts from a control switch signal, which starts the air-conditioningdevice.

At step 2510, the air-conditioning ECU 19 is reset. At step 2520, theECU 2019 inputs the output signals from each sensor such as the internalair temperature Tr, the external air temperature Tam, and the signalsfrom the temperature setter 2021.

At step 2530, the temporary target blow-out temperature TAOB iscalculated via the neural network 2200, shown in FIG. 20. The step 2530corresponds to the temporary target temperature calculating portion 2028in FIG. 17.

At step 2540, the radiation amount correction TAOS is calculated via theneural network 300, shown in FIG. 21. The step 2540 corresponds to theradiation amount correction calculating portion 2029 in FIG. 17.

At step 2550, the final target blow-out temperature TAO is calculated bythe equation (3). The step 2550 corresponds to the second targettemperature calculating portion 2030 in FIG. 17.

At step 2560, the air-mixing door opening degree SW is calculated by theequation (4). The step 2560 corresponds to the air-mixing door openingdegree calculating portion 2031 in FIG. 17.

At step 2570, the blow-out port mode signal TMODE is calculated via theneural network 2400, shown in FIG. 22. The step 2570 corresponds to theblow-out port mode calculating portion 2032 in FIG. 17. Here, outputs inthe blow-out port mode signal TMODE have characteristics that thisoutput increases in proportion to an increasing of the target blow-outtemperature TAO. One of a face mode (FACE), a bi-level mode (B/L), and afoot mode (FOOT), which are shown in FIG. 24, is determined based on theoutput TMODE from the neural network 2400.

At step 2580, the blower voltage TBLO₁ for the FACE, B/L modes and theblower voltage TBLO₂ for the FOOT mode are calculated, independently,via the neural network 2100, shown in FIG. 18. The step 2580 correspondsto the air amount calculating portion 2033 in FIG. 17.

At step 2590, one of the blower voltage TBLO₁ and the blower voltageTBLO₂ is selected based on the blow-out port mode TMODE calculated inthe step 2570. The step 2590 corresponds to the air amount selectingportion 2034 in FIG. 17.

At steps 2600-2620, each of actuators such as motors 2014-2018 and themotor applied voltage control circuit 2020 as shown in FIG. 16 arecontrolled so as to correspond to the above-mentioned calculated values.

FIG. 25 shows the relation between the blower voltage level for decidingthe air amount and the temperature difference TD (=Tr−Tset), asdescribed the above. The temperature difference TD of the abscissa canbe replaced with the internal air temperature Tr. When the internal airtemperature is used as the abscissa, the right side of the abscissarepresents a high temperature side, and the left side of the abscissarepresents a low temperature side.

An operation when the FOOT mode is selected in a heating operation inwinter will be explained. At just after the heating starts, the blower2003 starts with the blower voltage=E₁, as shown in FIG. 25. After theinternal air temperature Tr rises and reaches T₁, the blower voltagedecreases from E₁ in proportion to temperature rising. After theinternal air temperature reaches T₂, and approaches the setpointtemperature Tset, the operation is set to a normal operation area A, andthe blower voltage is set to the minimum voltage E₂.

An operation when the FACE mode is selected in a cooling operation insummer will be explained. At just after the cooling starts, the blower2003 starts with the blower voltage=the maximum voltage E₃ (E₃>E₁).After the internal air temperature Tr falls and reaches T₃, the blowervoltage decreases from E₃ in proportion to temperature falling. Afterthe internal air temperature reaches T₄, and approaches the setpointtemperature Tset, the operation is set to a normal operation area A, andthe blower voltage is set to voltage E₄.

Here, the voltage E₄ is set to high comparing to the voltage E₂, whichis the FOOT mode voltage, so that the air amount increases in proportionto the radiation amount. The air amount can be increased in proportionto the voltage difference ΔE between the voltage E₄ and voltage E₂, andcan be increased a cooling feeling of the passenger during the radiationin the FACE mode.

In FIG. 25, a blower voltage characteristic of the FACE mode has avoltage change of E₃→E₄→E₁, and one of them is determined by the blowervoltage level TBLO₁. A blower voltage characteristic of the B/L modeusing in intermediate season such as spring or autumn is the same asthat of FACE mode so that the cooling feeling of the passenger can beincreased during the radiation.

The voltage difference ΔE between the voltage E₄ and voltage E₂continuously change in proportion to the amount on radiation Ts.Therefore, when the FACE mode or the B/L mode and the radiation amountTs=0, the blower voltage decreases to E₂ level and then set to the samevoltage as that of the FOOT mode at the normal operation area A.

A blower voltage characteristic of the FOOT mode has a voltage change ofE₁→E₂→E₃ independent of the radiation amount, and one of them isdetermined by the blower voltage level TBLO₂.

Here, during the normal operation area A, if there is the radiation,when the blow-out port mode is changed among the FACE mode, B/L mode andthe FOOT mode, the blower voltage may be changed step by step. However,in this embodiment, the blower voltage level TBLO₁ for the FACE mode orB/L mode and the blower voltage level TBLO₂ for the FOOT mode are alwayscalculated via the neural network 2100, independently. Hence, the blowervoltage level TBLO₁ for the FACE mode or B/L mode can be changedcontinuously in proportion to the radiation amount Ts.

Therefore, since it is not needed to change the output step by stepduring the calculation of the blower voltage level TBLO₁, the learningfor calculating the link coefficient 2106 of the neural network 2100 canbe simplified.

Furthermore, in this embodiment, the temperature difference TD(=Tr−Tset) between the setpoint temperature Tset and the internal airtemperature Tr is calculated, and then the calculated value is input tothe neural network 2100. Therefore, the number of input to the neuralnetwork 2100 can be decreased compared to an input way inputting both ofthe Tset and the Tr.

Here, it may need to input both of the setpoint temperature Tset and theinternal air temperature Tr to detect whether the operation area is in atransition area, which is just after the air-conditioning device starts(the internal air temperature is changing to the setpoint temperature),or the normal operation area.

However, in this embodiment, this condition (the operation area is inthe normal operation area) can be detected from the fact that thetemperature difference TD between the internal air temperature Tr andthe setpoint temperature Tset reaches substantially zero. Therefore,this embodiment can easily detect whether the transition area or thenormal operation by inputting the temperature difference TD.

Since the number of input is decreased, the number of the layer in theneural network can be also decreased, the link coefficient can bedecreased by about 25%, and the total learning time for calculating thelink coefficient 2106 can be decreased largely.

It is acceptable to input both the setpoint temperature Tset and theinternal air temperature Tr replaced with the temperature difference TDto the neural network 2100, if an increasing of the number of the inputwould be less than allowable condition.

The two blower voltage levels TBLO₁ and TBLO₂ can be calculated by usingindependent two neural networks 2100 replaced with the calculation byusing one neural network 2100. However, it is preferable to use oneneural network 2100 as this embodiment, because using one neural network2100 does not need further process to set two neural networkindependently or further memories.

According to an investigation on an air amount control at the transitionarea just after the air-conditioning device starts in summer (so-calledcool down), the following facts are found. That is, at the point theblower voltage decreases from E₃ (TD=T₃) in FIG. 25, an air-conditioningfeeling for the passenger can be further improved by changing the blowervoltage level based on a heat load condition at the start timing of theair-conditioning device. This mechanism will be explained hereinafterwith reference to FIGS. 26-29.

FIGS. 26-29 show a characteristic of a target blower voltage (airamount) based on an average of the air amount, of which a plurality ofmonitor people feels comfort. In FIGS. 26-29, the ordinate representsthe blower voltage (V), the abscissa represents the temperaturedifference TD (° C.).

A result shown in FIG. 26 is measured under the following heat loadcondition. That is, at the start timing of the air-conditioning device,the external air temperature Tam is 20° C., the radiation amount Ts is1000 W/m², initial internal air temperature Tr is 50° C., and thesetpoint temperature Tset is 25° C. An air-conditioning load due to bothof the external air temperature Tam and the radiation amount Ts islargest in this condition among conditions shown in FIGS. 26-29.

As shown in FIG. 26, it takes long time to reduce the temperaturedifference TD of which just after air-condition starts, when theair-conditioning load at the start timing of the air-conditioning deviceis large. Therefore, the blower voltage (air amount) is kept its maximumvalue (e.g., 13.3V) for a long time. In this situation, cooled air fromthe face blow-out ports 2009 a-2009 d is kept blowing to the passenger'sface strongly for a while, when the FACE mode is selected. Then, thepassenger may not feel comfort due to an excessive cooled air.

Therefore, in the case when the air-conditioning load at the starttiming of the air-conditioning device is large, a decreasing point of TDat which starts to decrease the blower voltage is set large so that theblower voltage starts to decrease early. That is, the blower voltage(amount on air) starts to decrease at the point the TD=12° C.

A result shown in FIG. 27 is measured under the following heat loadcondition. That is, at the start timing of the air-conditioning device,the external air temperature Tam is 20° C., the radiation amount Ts is500 W/m², initial internal air temperature Tr is 40° C., and thesetpoint temperature Tset is 25° C. An air-conditioning load due to bothof the external air temperature Tam and the radiation amount Ts is inintermediate. In this characteristic, the blower voltage (amount on air)starts to decrease at the point the TD=9.5° C.

A result shown in FIG. 28 is measured under the following heat loadcondition. That is, at the start timing of the air-conditioning device,the external air temperature Tam is 10° C., the radiation amount Ts is1000 W/m², initial internal air temperature Tr is 40° C., and thesetpoint temperature Tset is 25° C. An air-conditioning load due to bothof the external air temperature Tam and the radiation amount Ts is alsoin intermediate. In this characteristic, the blower voltage (amount onair) starts to decrease at the point the TD=10° C.

A result shown in FIG. 29 is measured under the following heat loadcondition. That is, at the start timing of the air-conditioning device,the external air temperature Tam is 10° C., the radiation amount Ts is500 W/m², initial internal air temperature Tr is 40° C., and thesetpoint temperature Tset is 25° C. An air-conditioning load due to bothof the external air temperature Tam and the radiation amount Ts issmallest. In this characteristic, the blower voltage (amount on air)starts to decrease at the point the TD=7.5° C.

As described above, the decreasing point of TD during the cool down isset gradually small in proportion to decreasing the air-conditioningload at the start timing of the air-conditioning device. Then, it candetermine a suitable period that the blower voltage is set at themaximum value during the cool down, and a feeling of the cooling can beimproved.

Here, in FIGS. 26-29, the air amount is based on the temperaturedifference TD. However, one of the target blow-out temperature TAO, theinternal air temperature Tr and so on can be used to determined to theair amount replaced with the temperature difference TD. That is, boththe temperature difference TD and the target blow-out temperature Trchange in proportion to the change of the internal air temperature Trduring the cool down. Therefore, another data can be replaceable withthe temperature difference TD as long as it changes in proportion to thechange of the internal air temperature.

Further, in FIGS. 26-29, the characteristic of the air amount during thecool down in the FACE mode is explained. However, the passenger maymanually switch the blow-out port mode to B/L mode during a high heatload condition in summer. Therefore, these characteristics are adaptableto the high heat load condition in the B/L mode.

Here, the neural network has the learning function, which adjusts thelink coefficients 2106 (synapse weights) between each neurons in eachlayers in the neural network automatically to correct its output to bedesired data. Therefore, the output at a specific input condition can beadjusted, by changing the teacher signal at the specific input conditionand then adjusting the link coefficients 2106 (synapse weights)automatically in advance.

Furthermore, since the neural network adjusts its whole linkcoefficients 2106 so that the desired outputs at the other inputcondition are maintained even if the output at the specific inputcondition is changed. Therefore, the output change at the specific inputcondition does not influence the outputs at the other input conditions.Consequently, desired characteristic of the air amount can be achievedeasily for a wide range of input change due to a change of automobilecircumstance condition.

(Third Embodiment)

In this embodiment, the present invention is adopted to anair-conditioning device for automobiles, which is capable of controllingindependently between a driver seat (Dr) side air-conditioning zone anda passenger seat (Pa) side air-conditioning zone in a passengercomponent.

In an air-duct 2002, a partitioning wall 2002 a is arranged from theheater core 2005 portion to its downstream side in the air duct 2002 todivide it into a Dr side air passage 2002 b and a Pa side air passage2002 c.

A Dr side air-mixing door 2061 is arranged at the upstream side of theheater core 2005 in the Dr side air passage 2002 b. The Dr sideair-mixing door 2061 adjusts a ratio of two air amount in the Dr sideair passage 2002 b, an amount of heated air passed through the heatercore 2005 and an amount of cooled air by-passed the heater core 2005. APa side air-mixing door 2062 is arranged at the upstream of the heatercore 2005 in the Pa side air passage 2002 c, and adjusts a ratio of twoair amount in the Pa side air passage 2002 c. Here, the two air amountare an amount of heated air passed through the heater core 2005 and anamount of cooled air by-passed the heater core 2005.

At the most downstream side of the Dr side air passage 2002 b, a footblow-out port 2008 a, face blow-out ports 2009 a, 2009 b are provided.At the most downstream side of the Pa side air passage 2002 c, a footblow-out port 2008 b, face blow-out ports 2009 c, 2009 d are provided.Blowout port change-over doors 2011 a, 2012 a are arranged toselectively open/close the Dr side foot blow-out port 2008 a and theface blow-out ports 2009 a, 2009 b. Blowout port change-over doors 2011b, 2012 b are arranged to selectively open/close the Pa side footblow-out port 2008 b and the face blow-out ports 2009 c, 2009 d. Adefroster blow-out port 2010 and its blow-out port change-over door 13are provided in the same way as the second embodiment.

The Dr side air-mixing door 2061 and the Pa side air-mixing door 2062are driven by independent servomotors 2015 and 2015 a. The Dr sideblow-out port change-over doors 2011 a, 2012 a and the Pa side blow-outport change-over doors 2011 b, 2012 b are driven by independentservomotors 2160 and 2170. Each of predetermined blow-out mode, such asa FACE mode (FACE), a BI-LEVEL mode (B/L mode), and a FOOT mode (FOOT)or the like can be set with respective to each ports 2002 c, 2002 c,independently, by changing the open/close condition of the blow-out portchange-over doors 2011 a, 2012 a, 2011 b and 2012 b.

A Dr side temperature setter (first temperature setter) 2021 a isprovided to set a setpoint temperature Tset(Dr) of the Dr sideair-conditioning zone relative to the Dr side air passage 2002 b, andoutput the setpoint temperature Tset(Dr) to the ECU 2019. A Pa sidetemperature setter (second temperature setter) 2021 b is provided to seta setpoint temperature Tset(Pa) of the Pa side air-conditioning zonerelative to the Pa side air passage 2002 c, and output the setpointtemperature Tset(Pa) to the ECU 2019. Both temperature setters 2021 b,2021 c are provided independently each other.

A Dr side radiation sensor 2024 a is arranged to detect a radiationamount TsDr to the Dr side air-conditioning zone, and a Pa sideradiation sensor 2024 b is arranged to detect a radiation amount TsPa tothe Pa side air-conditioning zone.

As temperature data detectors, an internal air sensor 2022 to isarranged detect an internal air temperature Tr. An external air sensor2023 is arranged to detect an external air temperature Tam. Anevaporator temperature sensor 2025 is arranged to detect a coolingtemperature (blow-out air temperature) of the evaporator 2004. A watertemperature sensor 2026 is arranged to detect a temperature Tw of hotwater flowing into the heater core 2005.

Control functions processed by the microcomputer in the ECU 2019 areexecuted independently between the Dr side and the Pa side as shown inFIG. 31. In this figure, subscript “a” after symbol means Dr sidefunctions, and “b” after symbol means Pa side functions.

In this embodiment, a blow-out port mode signal TMODE common to both ofthe Dr side and the Pa side, are calculated by a blow-out port modecalculating portion 2032 so that the blow-out port modes of the Dr sideand the Pa side are set to be equal.

A blower voltage TBLO, which decides an air amount, calculated asfollows. As to the Dr side, a blower voltage level TBLO(Dr)₁ in the FACEmode or B/L mode and a blower voltage level TBLO(Dr)₂ in the FOOT modeare calculated by an air amount calculating portion 2033 a. As to the Paside, a blower voltage level TBLO(Pa)₁ in the FACE mode or B/L mode anda blower voltage level TBLO(Pa)₂ in the FOOT mode are calculated by anair amount calculating portion 2033 b. Then, an air amount selectingportion 2034 a selects one of the blower voltage TBLO(Dr)₁ and theblower voltage TBLO(Dr)₂ based on the blow-out port mode, and output itas a blower voltage TBLO(Dr). An air amount selecting portion 2034 bselects one of the blower voltage TBLO(Pa)₁ and the blower voltageTBLO(Pa)₂ based on the blow-out port mode, and output it as a blowervoltage TBLO(Pa). Finally, an average of the blower voltage TBLO(Dr) andthe blower voltage TBLO(Pa) are calculated by an air amount decidingportion 2035. The averaged value is output as a final blower voltageTBLO.

As shown in FIG. 32, a neural network 2100, which constitutes each theDr side air amount calculating portion 2033 a and the Pa side air amountcalculating portion 2033 b, inputs temperature differences TDi of eachthe Dr side and the Pa side, and radiation amount Tsi of each the Drside and the Pa side.

As shown in FIG. 33, a neural network 2200, which constitutes each a Drside temporary target temperature calculating portion 2028 a and a Paside temporary target temperature calculating portion 2028 b, inputssetpoint temperatures Tset(Dr) and Tset(Pa) of each the Dr side and thePa side, and further inputs a difference (ΔTset) between the setpointtemperatures Tset(Dr) and Tset(Pa). By inputting the difference (ΔTset),it can restrict a temperature interference between the firstair-conditioning zone and in the second air-conditioning zone.

As shown in FIG. 34, a neural network 2300, which constitutes each a Drside radiation amount correction calculating portion 2029 a and a Paside radiation amount correction calculating portion 2029 b, inputssetpoint temperatures Tset(Dr) and Tset(Pa) of each the Dr side and thePa side, and further input the Dr side radiation amount TsDr and the Paside radiation amount TsPa.

As shown in FIG. 35, a neural network 2400, which constitutes a blow-outport mode calculating portion 2032, inputs an average TAOX, which is anaveraged value of a Dr side target blow-out temperature TAO(Dr) and a Paside target blow-out temperature TAO(Pa), and further input an averageTsX, which is an averaged value of the Dr side radiation amount TsDr andthe Pa side radiation amount TsPa. Then, the neural network 2400calculates the blow-out port mode signal TMODE.

Here, in this embodiment, it is acceptable to control the Dr sideblow-out port mode and the Pa side blow-out port mode independently. Inthis case, the blow-out port mode calculating portion 2032 is dividedinto plurality of blow-out port mode calculating portions including a Drside blow-out port mode calculating portion 2032 a and Pa side blow-outport mode calculating portion 2032 b. As shown in FIG. 36, the neuralnetwork 2400, which constitutes each the blow-out port mode calculatingportions 2032 a, 2032 b, inputs the Dr side target blow-out temperatureTAO(Dr) or the Pa side target blow-out temperature TAO(Pa), and furtherinputs the Dr side radiation amount TsDr or the Pa side radiation amountTsPa. Then, each neural network 2400 calculates a Dr side blow-out portmode signal TMODE(Dr) and a Pa side blow-out port mode signal TMODE(Pa),independently.

Then, the air amount selecting portions 2034 a, 2034 b, shown in FIG.31, select the blower voltage TBLO(Dr) and the blower voltage TBLO(Dr)based on each the Dr side blow-out port or the Pa side blow-out portmode. Finally, an average of the blower voltage TBLO(Dr) and the blowervoltage TBLO(Pa) are calculated by an air amount deciding portion 2035.The averaged value is output as a final blower voltage TBLO.

The second and third embodiments can be also modified as follows.

(1) In the above-described embodiment, the temporary target blow-outtemperature TAOBi, the radiation amount correction TAOSi and theblow-out port mode TMODEi are calculated via the neural networks 2200,2300, 2400. However, it is acceptable to calculate one of the temporarytarget blow-out temperature TAOBi, the radiation amount correction TAOSiand the air amount by conventional calculation without neural networks.

(2) The learning method of the neural network can be replaceable withSemi Newton method or the like instead of the above-mentioned backpropagation method.

(3) The air-mixing doors 2006, 2061, 2062 are arranged to adjust the mixratio of cooled air and heated air as the temperature adjuster. However,it is replaceable with a hot water valve to control an amount of hotwater flowing into the heater core 2005 or a temperature of the hotwater.

(4) In the third embodiment, the average of the blower voltage TBLO(Dr)and the blower voltage TBLO(Pa) are calculated by the air amountdeciding portion 2035. The averaged value is output as the final blowervoltage TBLO. However, it is acceptable to calculate the final blowervoltage TBLO by putting an adequate weight to each the blower voltageTBLO(Dr) and the blower voltage TBLO(Pa) and adding or subtracting them.

(5) In the third embodiment, the blower 3 can be arranged in each Drside and Pa side independently. Further, an air provider can be arrangedin each the Dr side air passage 2002 b and the Pa side air passage 2002c to adjust each air amount, independently, so that each Dr side airamount and Pa side air amount can be controlled, independently.

(6) The temperature difference TD can be obtained by using just Tr andTset to calculate (Tr−Tset), or using another factor in addition to Trand Tset.

(7) As the temperature setter 2021, 2021 a, 2021 b for setting thetemperature of the air-conditioning zones, it can be used an analogdisplay in which indicates the temperature without digital figures,e.g., the temperature is indicated by colors.

(8) To adjust the air amount, the blower 3 is controlled by the voltagelevel applied thereto to control the its rotation speed. However, it isacceptable to control the rotation speed by a pulse wave modulation(PWM) method, which changes a duty ratio of a pulse voltage applied tothe blower motor 3 a.

(9) The present invention can be adaptable to another air-conditioningdevice, which has plurality of air-conditioning zone, and is needed tocontrol each of temperature of blow-out air to each zones independently.

(10) The neural networks 2100-2400 are replaceable with recurrent typeneural networks.

(Fourth Embodiment)

A fourth embodiment of the present invention will be describedhereinafter with reference to FIGS. 37-45 and 47C-50. FIG. 37 is adiagram illustrating the constitution of whole system of anair-conditioning device for automobiles. In FIG. 37, an internalair/external air change-over door 3001 is arranged at the most upstreamside of air flow in the air-conditioning device for automobiles. One ofan external air and an internal air is selectively introduced into anair duct 3002 by moving the door 3001.

The air duct 3002 constitutes an air passage of the air-conditioningdevice. A blower 3003, an evaporator 3004 and a heater core 3005 arearranged from upstream side to downstream side in the air duct 3002. Theevaporator 3004 is a cooling heat exchanger for cooling an air byabsorbing an evaporating latent heat of a refrigerant in a refrigeratingcycle from the air. The heater core 3005 is a heating heat exchanger forheating air with heat from a hot water (engine coolant) from a vehicleengine (not shown).

An air-mixing door 3006 as a temperature adjuster is arranged at theupstream of the heater core 3005. The air-mixing door 3006 adjusts aratio of two air amount, an amount of heated air passed through theheater core 3005 and an amount of cooled air by-passed the heater core3005. The adjusting the ratio of heated air and cooled air can adjuststhe blow-out air temperature to a passenger component of the automobile.

At the most downstream side of the air duct 3002, foot blow-out ports3008 a, 3008 b, face blow-out ports 3009 a-3009 d, and a defrosterblow-out port 3010 are provided. Here, the foot blow-out ports 3008 a,3008 b are provided to blow the conditioned air onto the feet of thepassengers. The face blow-out ports 3009 a-3009 d are provided at eachof center portion and side portion in the passenger component to blowthe conditioned air to the upper half of the body of the passengers. Thedefroster blow-out port 3010 is provided to blow the conditioned air toa windshield.

At the most downstream side of the air duct 3002, blow-out portchange-over doors 3011-3013 are arranged to selectively open/close theblow-out ports 3008 a, 3008 b and 3009 a-3009 d. Each of predeterminedblow-out modes, such a FACE mode (FACE), a BI-LEVEL mode (B/L), a FOOTmode (FOOT), and a defroster mode or the like can be set by changing theopen/close condition of the doors 3011-3013.

Here, a control system for controlling the air-conditioning device willbe explained. The internal air/external air changing door 3001, theair-mixing door 3006, and the blow-out port change-over doors are drivenby servomotors 3014-3018. The servomotors 3014-3018 are controlled byoutputs of an air-conditioning electric control device 3019(hereinafter, called “ECU 3019”). A motor 3 a of the blower 3003 is alsocontrolled by the output of the ECU 3019 via a motor control circuit(motor applied voltage control circuit) 3020.

An air amount blown by the blower 3003 is adjusted by the motor controlcircuit 3020 by way of changing a rotation speed of the motor with anapplied voltage to the motor. The ECU 3019 includes a microcomputer andits peripheral circuits.

A temperature setter 3021 is provided to set a setpoint temperature Tsetof the passenger component, which is input to the ECU 3019. Thetemperature setter 3021 is arranged an air-conditioning control panel,and is manually controlled by users.

As temperature data detectors, the following sensors are provided. Thatis, an internal air sensor 3022 is provided to detect an internal airtemperature Tr. An external air sensor 3023 is provided to detect anexternal air temperature Tam. A radiation sensor 3024 is provided todetect a radiation amount Ts to the passenger component. An evaporatortemperature sensor 3025 is provided to detect a cooling temperature Te(blow-out air temperature) of the evaporator 3004. A water temperaturesensor 3026 is provided to detect a water temperature Tw of hot waterflowing into the heater core 3005.

Control functions processed by the microcomputer in the ECU 3019 isgenerally divided as shown in FIG. 38. The ECU 3019 includes a temporarytarget temperature calculating portion 3027, a radiation amountcorrection calculating portion 3028, a target temperature calculatingportion 3029, an air-mixing door opening degree calculating portion3030, a blow-out port mode calculating portion 3031, and an air amountcalculating portion 3032.

Here, the temporary target temperature calculating portion 3027calculates a temporary target blow-out temperature, and includes aneural network 3200 as shown in FIG. 41. The temporary targettemperature calculating portion 3027 inputs signals including thesetpoint temperature Tset, the internal air temperature Tr and theexternal air temperature Tam, and calculates the temporary targetblow-out temperature TAOB based on the input signals via the neuralnetwork 3200.

The radiation amount correction calculating portion 3028 calculates aradiation amount correction TAOS, and includes a neural network 3300 asshown in FIG. 42. The radiation amount correction calculating portion3028 inputs signals including the setpoint temperature Tset, theinternal air temperature Tr, the external air temperature Tam and theradiation amount Ts (output signal of the radiation sensor 3024). Thenthe, radiation amount correction calculating portion 3028 calculates theradiation amount correction TAOS based on the input signals via theneural network 3300.

The target temperature calculating portion 3029 calculates the finaltarget blow-out temperature TAO based on the output signals from thetemporary target temperature calculating portion 3027 and the radiationamount correction calculating portion 3028.

The air-mixing door opening degree calculating portion 3030 calculatesan air-mixing door opening degree SW based on the final target blow-outtemperature TAO from the target temperature calculating portion 3029.

The blow-out port mode calculating portion 3031 calculates a blow-outport mode TMODE, and includes a neural network 3100 as shown in FIG. 39.The blow-out port mode calculating portion 3031 inputs the final targetblow-out temperature TAO from the target temperature calculating portion3029 and the detected information, which is the environment factor toinfluence a temperature sensitively of the passenger such as theradiation amount Ts, the external air temperature Tam and the watertemperature Tw. Then the blow-out port mode calculating portion 3031calculates the blow-out port mode TMODE via the neural network 3100.

The air amount calculating portion 3032 includes a neural network 3400as shown in FIG. 43. The air amount calculating portion 3032 inputssignals including the setpoint temperature Tset, the internal airtemperature Tr, the external air temperature Tam and the radiationamount. Then the air amount calculating portion 3032 calculates a blowervoltage, which decides an air amount via the neural network 3400.

The constitutions of the neural networks 3100-3400 will be explained.Since the constitutions of the neural networks 3100-3400 aresubstantially same, these constitutions is explained based on the neuralnetwork 3100 with reference to FIGS. 39, 40A, 40B.

As shown in FIG. 39, the neural network 3100 includes an input layer3101, a first intermediate layer 3102, a second intermediate layer 3103,and output layer 3104. Each of the input layer 3101, the first and thesecond intermediate layers 3102, 3103 has a plurality of neurons 3105,and output layer has one neuron 3105. Each of neurons 3105 in the inputlayer 3101 is connected to each of neurons 3105 in the firstintermediate layer 3102 with a certain link coefficient 3106 (synapseweight). Similarly, each of neurons 3105 in the first intermediate layer3102 is connected to each of neurons 3105 in the second intermediatelayer 3103 with a certain link coefficient 3106. Each of neurons 3105 inthe second intermediate layer 3103 is connected to the neuron 3105 inthe output layer 3104 with a certain link coefficient 3106. Theseneurons 3105 are formed by memories in the ECU 3019 in an actualcircuit, and the neural network 3100 is formed by combined memories.

The neural network 3100, which is a network having a layered structure,has a Back Propagation Learning Function. The Back Propagation LearningFunction can automatically correct the link coefficients 3106 betweeneach neurons among the input layer 3101, the first and secondintermediate layers 3102, 3103, and output layer, so as to adjust itsoutput to a desired value (teacher signal) related to input signals suchas TD, Ts, Tam, Tw.

When the teacher signal is changed, the link coefficients 3106 areadjusted by repeatedly “learning” so as to correct the output to be thechanged teacher signal related to the input signals. In other words,based on a large number of desired data (teacher signals), a correlationfunction (link coefficients 3106) is automatically generated. Theteacher signals are determined to desired values obtained by anexperience or the like (desired output for the input signals).

In the layered structure neural network 3100, there is no connectionamong each of the neurons 3105 in an identical layer, but the neurons3105 in each layer are connected to only neurons 3105 in forward andbackward layers. The clink coefficients 3106 between each neuron 3105 ineach layer represent a weight of connection (magnitude). The larger theweight of connection, the larger an amplitude of signal, which is outputfrom each neuron 3105 and propagated to neurons 3105 in backward layer.

Here, the input signals (TD, Ts, Tam, Tw) are normalized to 0-1 valuebefore inputting to the neural network 3100 by a normalize portion 3107.The output signal from the neural network 3100 is transformed in reverseprocedure of the normalize portion 3107 by an output transform portion3108. For example, actual detected internal temperature Tr detected bythe internal air sensor is normally in a range from 0° C. to 50° C. Thisdetected value is assigned to a certain value in a range from 0 to 1 inthe normalize portion 3107, and is output to the input layer 3101 in theneural network 3100. Since the output signal from the output layer 3104is a value in the range from 0 to 1, this output signal is transformedto the actual value corresponding to the sensor signal in the reverseprocedure by using a predetermined transformation map in the outputtransform portion 3108. Here, since the neural network 3100 in FIG. 39outputs the blow-out port signal TMODE, it does not need to reversetransform the output from the output layer 3104. Therefore, this neuralnetwork 3100 does not have the output transform portion 3108.

The environment conditions, of which the air-conditioning device forvehicle faces, are variable, as described above. Therefore, the numberof the teacher signals, which are desired values related to the variousenvironment conditions, will be enormously.

Therefore, in a designing process of the ECU 3019, the learning isexecuted by a high-speed calculator including a neural network, which isthe same as the neural network 3100, before mounted to the vehicle. Thehigh-speed calculator calculates the link coefficients 3106 so that theoutput of the neural network is to be desired teacher signals forvarious input signals. Then, the calculated coefficients 3106 arememorized in a memory portion (ROM) corresponding to each neural network3100, 3200, 3300, 3400 in each of the calculating portions of the ECU3019 shown in FIG. 38.

Therefore, when the ECU 3019 is mounted on the vehicle, the linkcoefficients 3106 among the neurons 3105 in each layer of the neuralnetworks 3100-3400 are set to predetermined value.

After mounted on the vehicle, the neural networks 3100-3400 in eachlayer calculate an output for input signals as shown in FIG. 40A.Specifically, in each of the neurons 3105, each of the input signalsO₁-O_(n) is multiplied by each of the corresponding coefficients 3106(W₁-W_(n)), and multiplied value are adapted to a so-called sigmoidfunction as shown FIG. 40B. The calculated results of the sigmoidfunction are output to the neurons 3105 in backward layer. Thesecalculations are repeated between each layer to decide a final output.

Since the neural network 3100 in each calculating portion of mounted ECU3019 is previously set so that the desired value for the various inputsignals O₁-O_(n) can be obtained, learning for correction of the linkcoefficients is not needed.

Therefore, the neural networks 3100 as shown in FIG. 39 of the blow-outport mode calculating portion 3031 calculates and outputs the desiredvalue as the blow-out port mode signal TMODE (=0-1) related to thechanges of input signals (TAO, Ts, Tam, Tw).

The neural network 3200 of the temporary target temperature calculatingportion 3027 outputs the desired teacher signals as the temporary targetblow-out temperature TAOB in relation to the change of the input signals(Tset, Tr, Tam).

The neural network 3300 of the radiation correction calculating portion3028 outputs the desired teacher signals as the radiation amountcorrection TAOS in relation to the change of the input signals (Tset,Tr, Tam, TS).

The neural networks 3100 of the air amount calculating portion 3032calculates and outputs the desired value as the blower voltage levelTBLO in relation to the changes of input signals (Tset, Tr, Tam, Ts).

In the target temperature calculating portion 3029, the final targetblow-out temperature TAO is calculated by the following equation:

TAO=TAOB−TAOS  (5)

In the air-mixing door opening degree calculating portion 3030, theair-mixing door opening degree SW is calculated based on the TAO fromthe target temperature calculating portion 3029, the temperature Te ofthe evaporator 3004, and the water temperature Tw of the heater core3005, by using the following equation:

Sw(%)=(TAO−Te)/(Tw−Te)×100  (6)

The control flow according to the preferred embodiment will be explainedwith reference to a flow chart shown in FIG. 44. This control routinestarts from a control switch signal, which starts the air-conditioningdevice.

At step 3510, the air-conditioning ECU 3019 is reset. At step 3520, theECU 3019 inputs the output signals from each sensor such as the internalair temperature Tr, the external air temperature Tam, and the signalsfrom the temperature setter 3021.

At step 3530, the temporary target blow-out temperature TAOB iscalculated via the neural network 3200, shown in FIG. 41. The step 3530corresponds to the temporary target temperature calculating portion 3027in FIG. 38.

At step 3540, the radiation amount correction TAOS is calculated via theneural network 3300, shown in FIG. 42. The step 3540 corresponds to theradiation amount correction calculating portion 3028 in FIG. 38.

At step 3550, the final target blow-out temperature TAO is calculated bythe equation (5). The step 3550 corresponds to the second targettemperature calculating portion 3030 in FIG. 38.

At step 3560, the air-mixing door opening degree SW is calculated by theequation (6). The step 3560 corresponds to the air-mixing door openingdegree calculating portion 3030 in FIG. 38.

At step 3570, the blow-out port mode signal TMODE is calculated via theneural network 3100, shown in FIG. 39. The step 3570 corresponds to theblow-out port mode calculating portion 3031 in FIG. 38. Here, outputs inthe blow-out port mode signal TMODE have characteristics that thisoutput increases in proportion to an increasing of the target blow-outtemperature TAO. One of a face mode (FACE), a bi-level mode (B/L), and afoot mode (FOOT), which are shown in FIG. 45, is determined based on theoutput TMODE from the neural network 3300.

At step 3580, the blower voltage TBLO is calculated via the neuralnetwork 3400, shown in FIG. 44. The step 3580 corresponds to the airamount calculating portion 3032 in FIG. 38.

At steps 3590-3610, each of actuators such as motors 3014-3018 and themotor applied voltage control circuit 3020 as shown in FIG. 37 arecontrolled so as to correspond to the above-mentioned calculated values.

The procedure of calculation of the blow-out port mode signal TMODE viathe neural network 3100 will be explained in detail. In FIG. 48, whichis a diagram of characteristic illustrating a relationship betweeninputs 1-4 and the output (teacher signal), the inputs 1-4 and theoutput correspond to TAO, Ts, Tam, Tw and TMODE. When the inputconditions are as follows, that is TAO=48° C., Ts=1000 W/², Tam=0° C.,Tw=80° C. (i.e., there is the radiation Ts and the external airtemperature Tam is low), and output is 0.95, the blow-out port modeTMODE is set the FOOT mode as shown in 47A. In this case, the passengermay feel hot to the head portion.

Then, the output signal (teacher signal) is changed from 0.95 to 0.5 atabove described input conditions.

Here, the neural network has the learning function, which adjusts thelink coefficients (synapse weights) 3106 between each neurons in eachlayers in the neural network automatically to correct its output to bedesired data (teacher signal). Therefore, the output at a specific inputcondition can be adjusted, by changing the teacher signal at thespecific input condition and then adjusting the link coefficients(synapse weights) 3106 automatically in advance.

By changing the output (TMODE=0.95→0.5), it enable to set the blow-outport mode to the B/L mode so as to blow-out the cooled air to the upperbody of the passenger, and enable to reduce the hot feeling due to theradiation to improve the air-conditioning feeling of the passenger.

Furthermore, since the neural network adjusts its whole linkcoefficients so that the desired outputs at the other input conditionare maintained even if the output at the specific input condition ischanged. Therefore, the output change at the specific input conditiondoes not influence the outputs at the other input conditions. Hence, inno radiation condition, it enable to set the blow-out port mode to theFOOT mode so as to blow-out the heated air to the foot area of thepassenger, and enable to improve the air-conditioning feeling of thepassenger.

Furthermore, since the correction of the link coefficients 3106 areadjusted automatically by using the learning function of the neuralnetwork, it will not be essential for the engineers to construct acomplicated control logic such that the desired output can be obtainedonly at specific input condition. Hence, engineer's process can beeliminated substantially.

Since the complicated process does not needed, a capacitance of a memory(ROM) of a computer, which constitutes an air-conditioning electriccontrol device, can be decreased.

Here, when the B/L mode is selected in the radiation condition at lowtemperature as shown in FIG. 47B, it is desirable to set an upperlimitation (A/M limiter) for the air-mixing door opening degree as shownin FIG. 47C.

That is, as shown in FIG. 47C, in a B/L mode extension area d in whichTAO is “a” or more, it is desirable to restrict a blowing of the heatedair from the face blow-out ports 3009 a-3009 d. In order to meet thisdesire, an actual blow-out temperature to the passenger component isrestricted to a predetermined temperature by setting the upperlimitation of the air-mixing door opening degree. Here, in this figure,a line “b” represents the face blow-out temperature and a line “c”represents the foot blow-out temperature.

Next, the switching control of the blow-out port mode in the followingcondition will be explained. That is, the condition that theair-conditioning device starts just after the vehicle engine starts at alow external temperature in winter will be explained. After the enginestarts, the temperature of an engine coolant (the temperature of the hotwater to the heater core 3005) rises. Then, the temperature of theblow-out air of the heater core 3005 rises. Further, the temperature ofblow-out air to the passenger component rises. Here, the voltage appliedto the motor of the blower is adjusted so that the air amount to thepassenger component increases in proportion to the rising of the hotwater temperature.

Here, when the blow-out port mode to the passenger component ismaintained to the FOOT mode, it may take long time to warm up the upperbody of the passenger because the body of the passenger is warmed upfrom his/her lower body. Hence, the passenger may not feel comfort.

Therefore, it is desirable to set the blow-out port mode to the FOOTmode initially, then set it to the B/L mode in proportion to the risingof the hot water temperature. By setting to the B/L mode, it enables toblow-out air to the upper body of the passenger from the face blow-outport so as to warm up the upper body early. Here, when theair-conditioning device starts at low external temperature, the controlof the air-conditioning device is set to a maximum heating. That is, theair-mixing door opening degree SW is set to its maximum degree (100%) sothat an air passage to the heater core 3005 is set to be full opened.

FIG. 49 shows a result of an experience to investigate a condition thatthe passenger feels the B/L mode comfort when the air-conditioningdevice starts at low external temperature. In this experience wasconducted on monitor people. According to the experience, the followingresults were obtained. As shown in this figure, there are certain rangesof the hot water temperature in which the passenger feels comfort(comfort range) in the B/L mode for each the external air temperatures.The comfort ranges of the B/L mode (ranges between a line Δ—Δ and a line□—□ in figure) are related to the external air temperature and the hotwater temperature. Specifically, the comfort ranges rise in proportionto falling of the external air temperatures. Here, ranges upper a line×—× are that the passenger feels hot, and ranges lower a line ◯—◯ arethat the passenger feels cold. Each the range between the line ×—× andthe line Δ—Δ, and ranges between the line □—□ and the line ◯—◯ areintermediate ranges that the passenger can not determine whether comfortor not.

Here, the temperature of air actually blown to the passenger componentis lowered to around 90% of the hot water due to a heat transferefficient between the hot water of the heater core 3005 and air.

Based on the experience, this embodiment provides the following controlfor the relationship between the inputs 1-4 of the neural network 3100and the output TMODE. That is, as shown in FIG. 50, when the inputs 1-4are TAO=80° C., Ts=0 W/m², Tam=−10° C., and when the Tw is within arange of 53° C. to 62° C., the output is changed from 0.95 to 0.5.Therefore, it enables to change the blow-out port mode from the FOOTmode to the B/L mode. Consequently, it enables to warm up the upper bodyof the passenger early within the range of Tw so as to improve theair-conditioning feeling and a driving safety.

In the other water temperature range, since the blow-out port mode canbe set to the FOOT mode by maintaining the output 0.95, it can preventover-cooling to the foot area in the B/L mode when the water temperatureis low. Also, it can prevent over-heating to the upper body in the B/Lmode when the water temperature is high.

(Fifth embodiment)

In the fourth embodiment, in the start timing at the low externaltemperature, the blow-out port mode is set to the B/L mode when the hotwater temperature is within the certain range. However, when theinternal air temperature Ts is already risen up due to radiation or thelike, it is desirable to change the blow-out port mode from the B/L modeto the FOOT mode early so as to prevent occurring the over-heating tothe upper body in the B/L mode.

Therefore, in this embodiment, the neural network 3100 further inputsthe internal air temperature Tr as its input as shown in FIG. 51. Achanging timing, at which the blow-out port mode is changed from the B/Lmode to the FOOT mode, is changed to early timing when the internal airtemperature rises over a predetermined temperature. Hence, it canrestrain occurring the over-heating to the upper body in the B/L mode inadvance. That is, even if the external air temperature is equal, whenthe internal air temperature Tr rises over the predeterminedtemperature, a threshold value (hot water temperature), at which theblow-out port mode is changed from the B/L mode to the FOOT mode, ischanged to low temperature side.

(Sixth embodiment)

The neural network 3100 of the fifth embodiment input the internal airtemperature Tr as an additional input. In this embodiment, the neuralnetwork 3100 inputs a skin temperature signal Th, which is output from askin temperature detecting sensor (not shown) for detecting a skintemperature of the passenger, replace with the internal air temperatureas shown in FIG. 52.

According to this embodiment, when the skin temperature Th of thepassenger rises over a predetermined temperature due to the radiation orthe like, a threshold value (hot water temperature), at which theblow-out port mode is changed from the B/L mode to the FOOT mode, ischanged to low temperature side. Hence, it can restrain occurring theover-heating to the upper body in the B/L mode in advance.

(Seventh embodiment)

The seventh embodiment is modification of the fourth embodiment. In thisembodiment, the neural network 3100 is equal to that shown in FIG. 39,which inputs the external air temperature Tam as an additional input. Inthis embodiment, a changing point of the blow-out port mode is changedwith respect to the external air temperature Tam.

When the TAO is in a high temperature side (heating area), the changingpoint (between the B/L mode and the FOOT mode) at the low external airtemperature (Tam=−10° C.) is set low than that at the high external airtemperature (Tam=10° C.) with respect to the TAO. Hence, it can enlargea FOOT mode area so as to perform the foot heating positively.Consequently, in can improve the heating feeling in the low external airtemperature.

When the TAO is in a low temperature side (cooling area), the changingpoint (between the B/L mode and the FACE mode) at the high external airtemperature (Tam=30° C.) is set high than that at the low external airtemperature (Tam=10° C.) with respect to the TAO. Hence, it can enlargea FACE mode area so as to perform the face cooling positively.Consequently, in can improve the cooling feeling in the high externalair temperature.

(Eighth embodiment)

In this embodiment, the present invention is adopted to anair-conditioning device for automobiles, which is capable of controllingindependently between a driver seat (Dr) side air-conditioning zone anda passenger seat (Pa) side air-conditioning zone in a passengercompartment.

In an air-duct 3002, a partitioning wall 3002 a is arranged from theheater core 3005 portion to its downstream side in the air duct 3002 todivide it into a Dr side air passage 3002 b and a Pa side air passage3002 c.

A Dr side air-mixing door 3061 is arranged at the upstream side of theheater core 3005 in the Dr side air passage 3002 b. The Dr sideair-mixing door 3061 adjusts a ratio of two air amount in the Dr sideair passage 3002 b, an amount of heated air passed through the heatercore 3005 and an amount of cooled air by-passed the heater core 3005. APa side air-mixing door 3062 is arranged at the upstream of the heatercore 3005 in the Pa side air passage 3002 c, and adjusts a ratio of twoair amount in the Pa side air passage 3002 c. Here, the two air amountare an amount of heated air passed through the heater core 3005 and anamount of cooled air by-passed the heater core 3005.

At the most downstream side of the Dr side air passage 3002 b, a footblow-out port 3008 a, face blow-out ports 3009 a, 3009 b are provided.At the most downstream side of the Pa side air passage 3002 c, a footblow-out port 3008 b, face blow-out ports 3009 c, 3009 d are provided.Blowout port change-over doors 3011 a, 3012 a are arranged toselectively open/close the Dr side foot blow-out port 3008 a and theface blow-out ports 3009 a, 3009 b. Blowout port change-over doors 3011b, 3012 b are arranged to selectively open/close the Pa side footblow-out port 3008 b and the face blow-out ports 3009 c, 3009 d. Adefroster blow-out port 3010 and its blow-out port change-over door 3013are provided in the same way as the second embodiment.

The Dr side air-mixing door 3061 and the Pa side air-mixing door 3062are driven by independent servomotors 3015 and 3015 a. The Dr sideblow-out port change-over doors 3011 a, 3012 a and the Pa side blow-outport change-over doors 3011 b, 3012 b are driven by independentservomotors 3160 and 3170. Each of predetermined blow-out mode, such asa FACE mode (FACE), a BI-LEVEL mode (B/L mode), and a FOOT mode (FOOT)or the like can be set with respective to each ports 3002 c, 3002 c,independently, by changing the open/close condition of the blow-out portchange-over doors 3011 a, 3012 a, 3011 b and 3012 b.

A Dr side temperature setter (first temperature setter) 3021 a isprovided to set a setpoint temperature Tset(Dr) of the Dr sideair-conditioning zone relative to the Dr side air passage 3002 b, andoutput the setpoint temperature Tset (Dr) to the ECU 3019. A Pa sidetemperature setter (second temperature setter) 3021 b is provided to seta setpoint temperature Tset(Pa) of the Pa side air-conditioning zonerelative to the Pa side air passage 3002 c, and output the setpointtemperature Tset(Pa) to the ECU 3019. Both temperature setters 3021 b,3021 c are provided independently each other.

A Dr side radiation sensor 3024 a is arranged to detect a radiationamount TsDr to the Dr side air-conditioning zone, and a Pa sideradiation sensor 3024 b is arranged to detect a radiation amount TsPa tothe Pa side air-conditioning zone.

As temperature data detectors, an internal air sensor 3022 to isarranged detect an internal air temperature Tr. An external air sensor3023 is arranged to detect an external air temperature Tam. Anevaporator temperature sensor 3025 is arranged to detect a coolingtemperature (blow-out air temperature) of the evaporator 3004. A watertemperature sensor 3026 is arranged to detect a temperature Tw of hotwater flowing into the heater core 3005.

Control functions processed by the microcomputer in the ECU 3019 areexecuted independently between the Dr side and the Pa side as shown inFIG. 31. In this figure, subscript “a” after symbol means Dr sidefunctions, and “b” after symbol means Pa side functions.

A blower voltage TBLO, which decides an air amount, calculated asfollows. A Dr side blower voltage level TBLO(Dr) and a Pa side blowervoltage level TBLO(Pa) are calculated by an air amount calculatingportions 3032 a, 3032 b. Then, an average of the blower voltage TBLO(Dr)and the blower voltage TBLO(Pa) are calculated by an air amountcalculating portion 3032 c. The averaged value is output as a finalblower voltage TBLO.

Blowout port mode calculating portions 3031 a, 3031 b are provided tocalculate a Dr side blow-out port mode signal TMODE(Dr) and a Pa sideblow-out port mode signal TMODE(Pa) independently.

A neural network 3100, which constitutes each the Dr side blow-out portmode calculating portion 3031 a and the Pa side blow-out port modecalculating portion 3031b, inputs one of the Dr side target blow-outtemperature TAO(Dr) and the Pa side target blow-out temperature TAO(Pa),and further inputs one of the Dr side radiation amount TsDr and the Paside radiation amount TsPa. Then, one of the Dr side blow-out port modesignal TMODE(Dr) and the Pa side blow-out port mode signal TMODE(Pa) iscalculated independently.

A neural network 3200, which constitutes each a Dr side temporary targettemperature calculating portion 3027 a and a Pa side temporary targettemperature calculating portion 3027 b, inputs one of setpointtemperatures Tset(Dr) and Tset(Pa) of each the Dr side and the Pa side,and further inputs a difference (ΔTset) between the setpointtemperatures Tset(Dr) and Tset(Pa). By inputting the difference (ΔTset),it can restrain a temperature interference between the firstair-conditioning zone and in the second air-conditioning zone.

Here, in this embodiment, it is acceptable to control both the Dr sideblow-out port mode and the Pa side blow-out port mode in the same way.That is, it does not need to control independently. In that case, asshown in FIG. 56, a neural network 3100 inputs an average TAOX, which isan averaged value of a Dr side target blow-out temperature TAO(Dr) and aPa side target blow-out temperature TAO(Pa), and further input anaverage TsX, which is an averaged value of the Dr side radiation amountTsDr and the Pa side radiation amount TsPa. Then, the neural network3100 calculates the blow-out port mode signal TMODE.

The fourth to eighth embodiments can be also modified as follows.

(1) In the above-described embodiment, the target blow-out temperatureTAOi, the air amount (blower voltage level) are calculated via theneural networks 3200, 3300, 3400. However, it is acceptable to calculateone of them by conventional calculation without neural networks.

(2) The air-mixing doors 3006, 3061, 3062 are arranged to adjust the mixratio of cooled air and heated air as the temperature adjuster. However,it is replaceable with a hot water valve to control an amount of hotwater flowing into the heater core 3005 or a temperature of the hotwater.

(3) In the eighth embodiment, the neural network 3100 inputs the averagevalue of the Dr side target blow-out temperature TAO(Dr) and the Pa sidetarget blow-out temperature TAO(Pa). However, it is acceptable to changeweights of one of the Dr side target blow-out temperature TAO(Dr) andthe Pa side target blow-out temperature TAO(Pa). That is, it acceptablefor neural network 3100 to input a changed target blow-out temperatureTAO′. For example, TAO′=TAO(Dr)×80(%)+TAO(Pa)×20%

(4) As the temperature setter 3021, 3021 a, 3021 b for setting thetemperature of the air-conditioning zones, it can be used an analogdisplay in which indicates the temperature without digital figures,e.g., the temperature is indicated by colors.

(5) The present invention can be adaptable to another air-conditioningdevice, which has plurality of air-conditioning zone, and is needed tocontrol each of temperature of blow-out air to each zones independently.

(6) The neural networks 3100-3400 are replaceable with recurrent typeneural networks.

(Ninth Embodiment)

A fourth embodiment of the present invention will be describedhereinafter with reference to FIGS. 57-64. FIG. 57 is a diagramillustrating the constitution of whole system of an air-conditioningdevice for automobiles.

In FIG. 57, an internal air/external air change-over door 4001 isarranged at the most upstream side of air flow in the air-conditioningdevice for automobiles. One of an external air and an internal air isselectively introduced into an air duct 4002 by moving the door 4001.

The air duct 4002 constitutes an air passage of the air-conditioningdevice. A blower 4003, an evaporator 4004 and a heater core 4005 arearranged from upstream side to downstream side in the air duct 4002. Theevaporator 4004 is a cooling heat exchanger for cooling an air byabsorbing an evaporating latent heat of a refrigerant in a refrigeratingcycle from the air. The heat core 4005 is a heating heat exchanger forheating air with heat from a hot water (engine coolant) from a vehicleengine (not shown).

An air-mixing door 4006 as a temperature adjuster is arranged at theupstream of the heater core 4005. The air-mixing door 4006 adjusts aratio of two air amount, wherein an amount of heated air passed throughthe heater core 4005 and an amount of cooled air by-passed the heatercore 4005. The adjusting the ratio of heated air and cooled air canadjusts the blow-out air temperature to a passenger component of theautomobile.

At the most downstream side of the air duct 4002, foot blow-out ports4008 a, 4008 b, face blow-out ports 4009 a-4009 d, and a defrosterblow-out port 4010 are provided. Here, the foot blow-out ports 4008 a,4008 b are provided to blow the conditioned air onto the feet of thepassengers. The face blow-out ports 4009 a-4009 d are provided at eachof center portion and side portion in the passenger component to blowthe conditioned air to the upper half of the body of the passengers. Thedefroster blow-out port 4010 is provided to blow the conditioned air toa windshield.

At the most downstream side of the air duct 4002, blow-out portchange-over doors 4011-4013 are arranged to selectively open/close theblow-out ports 4008 a, 4008 b and 4009 a-4009 d. Each of predeterminedblow-out modes, such a FACE mode (FACE), a BI-LEVEL mode (B/L mode), aFOOT mode (FOOT), and a defroster mode or the like can be set bychanging the open/close condition of the doors 4011-4013.

Here, a control system for controlling the air-conditioning device willbe explained. The internal air/external air changing door 4001, theair-mixing door 4006, and the blow-out port change-over doors are drivenby servomotors 4014-4018. The servomotors 4014-4018 are controlled byoutputs of an air-conditioning electric control device 4019(hereinafter, called “ECU 4019”). A motor 4003 a of the blower 4003 isalso controlled by the output of the ECU 4019 via a motor controlcircuit 4020 (motor applied voltage control circuit).

An air amount blown by the blower 4003 is adjusted by the motor controlcircuit 4020 by way of changing a rotation speed of the motor with anapplied voltage to the motor. The ECU 4019 includes a microcomputer andits peripheral circuits.

A temperature setter 4021 is provided to an air-conditioning controlpanel 4027 to set a setpoint temperature Tset of the passengercomponent, which is input to the ECU 4019. The temperature setter 4021is arranged an air-conditioning control panel, and is manuallycontrolled by users.

As temperature data detectors, the following sensors are provided. Thatis, an internal air sensor 4022 is provided to detect an internal airtemperature Tr. An external air sensor 4023 is provided to detect anexternal air temperature Tam. A radiation sensor 4024 is provided todetect a radiation amount Ts to the passenger component. An evaporatortemperature sensor 4025 is provided to detect a cooling temperature Te(blow-out air temperature) of the evaporator 4004. A water temperaturesensor 4026 is provided to detect a water temperature Tw of hot waterflowing into the heater core 4005.

Control functions processed by the microcomputer in the ECU 4019 isgenerally divided as follows. The ECU 4019 includes a temporary targettemperature calculating portion 4028, a radiation correction coefficientcalculating portion 4029, a radiation amount correction calculationportion 4030, a target temperature calculating portion 4031, an airamount calculating portion 4032, a suction port mode calculating portion4033, an blow-out port calculating portion 4034, and an air-mixing dooropening degree calculating portion 4035.

The temporary target temperature calculating portion 4028 calculates atemporary target blow-out temperature. The temporary target temperaturecalculating portion 4028 inputs signals including the setpointtemperature Tset, the internal air temperature Tr and the external airtemperature Tam, and calculates the temporary target blow-outtemperature TAOB based on the input signals by an equation (7) describedafter.

The radiation correction coefficient calculating portion 4029 calculatesa radiation correction coefficient Fs, and includes a neural network4100 as shown in FIG. 58. The radiation correction coefficientcalculating portion 4029 inputs signals including a temperaturedifference TD between the setpoint temperature Tset and the internal airtemperature Tr, and the external air temperature Tam. Then, theradiation correction coefficient calculating portion 4029 calculates theradiation correction coefficient Fs via the neural network 4100. Then,the radiation amount correction calculating portion 4030 calculates theradiation amount correction TAOS based on the radiation correctioncoefficient Fs and the amount of (solar) radiation Ts by using anequation described after.

The target temperature calculating portion 4031 calculates the finaltarget blow-out temperature TAO based on the output signals from thetemporary target temperature calculating portion 4028 and the radiationamount correction calculating portion 4030.

The air amount calculating portion 4032 inputs the target blow-outtemperature TAO and calculates a blower voltage for deciding an mount ofair from the blower based on a characteristic of FIG. 61 describedafter.

The suction port calculating portion 4033 inputs the target blow-outtemperature TAO and calculates a suction port mode of internal andexternal air based on a characteristic of FIG. 62 described after.

The blow-out port calculating portion 4034 inputs the target blow-outtemperature TAO and calculates a blow-out port mode of internal andexternal air based on a characteristic of FIG. 63 described after.

The air-mixing door opening degree calculating portion 4035 inputs thetarget blow-out temperature TAO and calculates an air-mixing dooropening degree SW by using an equation (10) described after.

The constitutions of the neural networks 4100, which constitutes theradiation correction coefficient portion 4029, will be explained withreference to FIG. 58.

As shown in FIG. 58, the neural network 4100 includes an input layer4101, a first intermediate layer 4102, a second intermediate layer 4103,and output layer 4104. Each of the input layer 4101, the first and thesecond intermediate layers 4102, 4103 has a plurality of neurons 4105,and output layer has one neuron 4105. Each of neurons 4105 in the inputlayer 4101 is connected to each of neurons 4105 in the firstintermediate layer 4102 with a certain link coefficient 4106 (synapseweight). Similarly, each of neurons 4105 in the first intermediate layer4102 is connected to each of neurons 4105 in the second intermediatelayer 4103 with a certain link coefficient 4106. Each of neurons 3405 inthe second intermediate layer 4103 is connected to the neuron 4105 inthe output layer 4104 with a certain link coefficient 4106. Theseneurons 4105 are formed by memories in the ECU 4019 in an actualcircuit, and the neural network 4100 is formed by combined memories.

The neural network 4100, which is a network having a layered structure,has a Back Propagation Learning Function. The Back Propagation LearningFunction can automatically correct the link coefficients 4106 betweeneach neurons among the input layer 4101, the first and secondintermediate layers 4102, 4103, and output layer, so as to adjust itsoutput to a desired value (teacher signal) related to input signals suchas TD or Tam.

When the teacher signal is changed, the link coefficients 4106 areadjusted by repeatedly “learning” so as to correct the output to be thechanged teacher signal related to the input signals. In other words,based on a large number of desired data (teacher signals), a correlationfunction (link coefficients 4106) is automatically generated. Theteacher signals are determined to desired values obtained by anexperience or the like (desired output for the input signals).

In the layered structure neural network 4100, there is no connectionamong each of the neurons 4105 in an identical layer, but the neurons4105 in each layer are connected to only neurons 4105 in forward andbackward layers. The clink coefficients 4106 between each neuron 4105 ineach layer represent a weight of connection (magnitude). The larger theweight of connection, the larger an amplitude of signal, which is outputfrom each neuron 4105 and propagated to neurons 4105 in backward layer.

Here, the input signals (TD, Tam) are normalized to 0-1 value beforeinputting to the neural network 4100 by a normalize portion 4107. Theoutput signal from the neural network 4100 is transformed in reverseprocedure of the normalize portion 4107 by an output transform portion4108. For example, actual detected internal temperature Tr detected bythe internal air sensor is normally in a range from −30° C. to 50° C.This detected value is assigned to a certain value in a range from 0 to1 in the normalize portion 4107, and is output to the input layer 4101in the neural network 4100. Since the output signal from the outputlayer 4104 is a value in the range from 0 to 1, this output signal istransformed to the actual value corresponding to the sensor signal inthe reverse procedure by using a predetermined transformation map in theoutput transform portion 4108.

The environment conditions, of which the air-conditioning device forvehicle faces, are variable, as described above. Therefore, the numberof the teacher signals, which are desired values related to the variousenvironment conditions, will be enormously.

Therefore, in a designing process of the ECU 4019, the learning isexecuted by a high-speed calculator including a neural network, which isthe same as the neural network 4100, before mounted to the vehicle. Thehigh-speed calculator calculates the link coefficients 4106 so that theoutput of the neural network is to be desired teacher signals forvarious input signals. Then, the calculated coefficients 4106 arememorized in a memory portion (ROM) corresponding to the neural network4100 in the radiation correction coefficient calculating portion 4029 ofthe ECU 4019 shown in FIG. 57.

Therefore, when the ECU 4019 is mounted on the vehicle, the linkcoefficients 4106 among the neurons 4105 in each layer are set topredetermined value.

After mounted on the vehicle, the neural network 4100 in ECU 4019calculate an output for input signals as shown in FIG. 59A.Specifically, in each of the neurons 4105, each of the input signalsO₁-O_(n) is multiplied by each of the corresponding coefficients 4106(W₁-W_(n)), and multiplied value are adapted to a so-called sigmoidfunction as shown FIG. 59B. The calculated results of the sigmoidfunction are output to the neurons 4105 in backward layer. Thesecalculations are repeated between each layer to decide a final output.

Since the neural network 4100 in each calculating portion of mounted ECU4019 is previously set so that the desired value for the various inputsignals O₁-O_(n) can be obtained, learning for correction of the linkcoefficients does not needed.

Therefore, the neural networks 4100 of the radiation correctioncoefficient calculating portion 4029, as shown in FIG. 57, calculatesand outputs the desired value as the radiation correction coefficient Fsin relation to the changes of input signals (TD, Tam).

The control flow according to the preferred embodiment will be explainedwith reference to a flow chart shown in FIG. 60. This control routinestarts when the ECU 4019 is powered by turning on an ignition switch(not shown) of the Vehicle.

At step 4510, a memory or the like in the ECU 4019 are initialized. Atstep 4520, the ECU 4019 inputs signals from control switches includingthe temperature setter 4021 of the air-conditioning control panel 4027and so on.

At step 4530, the ECU 4019 inputs sensor signals from the sensors(4022-4026).

At step 4540, the temporary target blow-out temperature TAOB iscalculated by eliminating a radiation term in the following equation(7). The step 4540 corresponds to the temporary target temperaturecalculating portion 4028 in FIG. 57.

TAOB=Kset×Tset−Kr×Tr−Kam×Tam+C  (7)

Here, Tset is the setpoint temperature, Tr is the internal airtemperature, Tam is the external air temperature, Kset is a temperatureset gain, Kr is an internal air temperature gain, Kam is an external airtemperature gain, and C is an correction constant value.

At step 4550, the ECU 4019 inputs external air temperature and thetemperature difference TD between the setpoint temperature Tset, andcalculates the radiation correction coefficient Fs. The step 4550corresponds to the radiation correction coefficient calculating portion4029 in FIG. 57.

At step 4560, the radiation amount correction TAOS is calculated bymultiplying the radiation correction coefficient Fs, the radiation Tsfrom the radiation sensor 4024, and a radiation correction proportionalgain Ks. The step 4560 corresponds to the radiation amount correctioncalculating portion 4030 in FIG. 57.

TAOS=Ks×Fs×Ts  (8)

Here, the radiation amount correction TAOS can be adjusted by adjustingthe radiation correction proportional gain Ks in the equation (8).Therefore, when the radiation amount TAOS needs to be adjusted based onan air-conditioning feeling result of actual running test, the radiationamount correction TAOS can be adjusted by the radiation correctionproportional gain Ks without changing the radiation correctioncoefficient Fs. Therefore, it does not need to have the neural networklearn again to adjust the radiation amount correction TAOS.

At step 4570, the target blow-out temperature TAO is calculated based onthe temporary target blow-out temperature TAOB And the radiation amountcorrection TAOS by using the following equation (9). The step 4570corresponds to the target blow-out temperature calculating portion 4031in FIG. 57.

TAO=TAOB−TAOS  (9)

At step 4580, the blower voltage is calculated based on the targetblow-out temperature TAO with reference to a relationship (map) shown inFIG. 61. The step 4580 corresponds to the air amount calculating portion4032 in FIG. 57.

At step 4590, the suction port modes of each the internal air, theexternal air, and the half internal air are calculated based on thetarget blow-out temperature TAO with reference to a relationship (map)shown in FIG. 62. The step 4590 corresponds to the suction port modecalculating portion 4033 in FIG. 57.

At step 4600, the blow-out port mode including FACE, B/L, FOOT mode iscalculated based on the target blow-out temperature TAO with referenceto a relationship (map) shown in FIG. 63. The step 4600 corresponds tothe blow-out port mode calculating portion 4034 in FIG. 57.

Here, the relationships (maps) of FIGS. 61-63 are memorized previouslyin memories in the ECU 4019.

At step 4610, the air-mixing door opening degree SW is calculated basedon the target blow-out temperature TAO, the temperature Te of theevaporator 4004, and a hot water temperature Tw of the heater core 4005by using the following equation (10). The step 4610 corresponds to theair-mixing door opening degree calculating portion 4035 in FIG. 57.

SW(%)=(TAO−Te)/(Tw−Te)×100  (10)

At step 4620, the ECU 4019 outputs the values calculated at the steps4580-4610 as control signals to each of actuators such as motors4014-4018 and the motor applied voltage control circuit 4020 as shown inFIG. 57. Then, at step 4630, the ECU 4019 wait for passing a certaintime “t”. After the time “t” is over, it returns to the step 4520.

In this embodiment, the radiation correction coefficient Fs iscalculated via the neural network 4100, and the radiation amountcorrection TAOS is calculated by multiplying the radiation correctioncoefficient FS, radiation signal Ts, and radiation correctionproportional gain Ks. A merit of calculation using the above-mentionedprocedure will be explained hereinafter.

FIG. 64 shows a calculating procedure of the radiation correctioncoefficient Fs in a start timing of heating in winter. The abscissarepresents a temperature difference TD (=Tr−Tset) between the internalair temperature Tr in the passenger component and the setpointtemperature Tset. This difference TD is in a minus because the internalair temperature Tr is lower than the setpoint temperature Tset.

When the external air temperature Tam is rather high (ex. 10° C.), theradiation correction is started from the point TD=−a. That is, theradiation correction efficient Fs starts to rise from 0 from the pointTD=−a (at a point the internal air temperature is lower than thesetpoint temperature by temperature “a”). When the external airtemperature Tam is rather low (ex. −10° C.), the radiation correction isstarted from the point TD=−b. That is, the radiation correctionefficient Fs starts to rise from 0 from the point TD=−b (at a point theinternal air temperature is lower than the setpoint temperature bytemperature “b”).

In this way, the start point of radiation correction is delayed as theexternal air temperature is low. That is, the radiation correction isdelayed until the internal air temperature reaches the setpointtemperature substantially. This delaying can keep the target blow-outtemperature high in the start timing of heating in winter. That is, evenif the temperature difference is equal value, the warm-up time can beshortened by decreasing the radiation amount correction so as to keepthe TAO high when the external air temperature is low.

(Tenth Embodiment)

FIGS. 65A, 65B correspond to FIG. 64, and show a characteristic of aradiation correction according to the tenth embodiment. FIG. 65A issubstantially equal to FIG. 64. In FIG. 65A, KFs represents a maximumvalue of the radiation correction coefficient Fs. FIG. 65B shows thatthe maximum value KFs of the radiation correction coefficient Fs isfurther changed in proportion to the external air temperature Tam.

As shown in FIG. 65A, the maximum value KFs is set in normal operationregion of heating mode, when the temperature difference TD reaches zeroas the internal air temperature reaches the setpoint temperature Tset ina start timing of heating in winter. Here, since an angle of the sun israther small in winter, the solar radiation is likely to be radiated toupper body of the passenger. Then, the passenger may feel hot due to theradiation.

Therefore, in this embodiment, during the normal operation, the maximumvalue KFs is increased from 1.0 to 1.2 as the external air temperatureTam falls from 20° C. so as to increase the radiation amount correction.Hence, the TAO can be set to low temperature so as to set the blow-outport mode to B/L mode to blow cooled air from a face blow-out port.Then, the air-conditioning feeling can be improved.

(Eleventh Embodiment)

The radiation correction coefficient Fs according to the eleventhembodiment is shown in FIG. 66. As shown in FIG. 67, the neural network4100 of the radiation correction coefficient calculating portion 4029inputs the radiation amount Ts in addition to the temperature differenceTD and the external air temperature Tam.

In this embodiment, the start point of the radiation correction isdelayed as the external air temperature Tam is low. Furthermore, whenthere is little radiation amount, the start point of the radiationcorrection is further delayed. Since this embodiment decides theradiation correction efficient Fs based on both the external airtemperature Ts and the radiation amount Ts, the radiation amountcorrection can be calculated more accurately. Then, the warm-up time inthe start timing of heating in winter can be shortened.

In this embodiment, it is acceptable that the maximum value KFs isincrease as the external air temperature Tam falls so as to increase theradiation amount correction like the characteristic shown in FIG. 65B.Then, the air-conditioning feeling in the normal operation can beimproved.

FIG. 68 is a diagram illustrating a relation between inputs 1-3 and anoutput (teacher signal) Fs of the neural network 4100. As shown in FIG.68, when the temperature difference TD as the input1 is equal (−5° C.,0° C.), and the radiation amount Ts as the input 3 is equal (500 W/m²),and the external air temperature is 10° C., the maximum value KFs isincreased from 1.0 to 1.2 so as to obtain a desired radiation correctioncoefficient Fs and the desired radiation amount correction TAOS.

Here, the neural network has the learning function, which adjusts thelink coefficients (synapse weights) 4106 between each neurons in eachlayers in the neural network automatically to correct its output to bedesired data (teacher signal). Therefore, the output at a specific inputcondition can be adjusted, by changing the teacher signal at thespecific input condition and then adjusting the link coefficients 4106automatically in advance.

By above-mentioned changing of output (change of the Fs from 1.0 to1.2), the heat feeling of the passenger (user) due to the radiation tothe his/her upper bodies can be reduced so as to improve theair-conditioning feeling of the passenger.

Furthermore, since the neural network adjusts its whole linkcoefficients 4106 so that the desired outputs (teacher signal) at theother input condition are maintained even if the output at the specificinput condition is changed. Therefore, the output change at the specificinput condition does not influence the outputs at the other inputconditions.

Furthermore, since the correction of the link coefficients 4106 areadjusted automatically by using the learning function of the neuralnetwork, it will not be essential for the engineers to construct acomplicated control logic such that the desired output can be obtainedonly at specific input condition. Hence, engineer's process can beeliminated substantially.

Since the complicated process does not needed, a capacitance of a memory(ROM) of the ECU 4019, which constitutes an air-conditioning electriccontrol device, can be decreased.

Furthermore, in this embodiment, the temperature difference TD(=Tr−Tset) between the setpoint temperature Tset and the internal airtemperature Tr is calculated, and then the calculated value is input tothe neural network 4100. Therefore, the number of input to the neuralnetwork 4100 can be decreased compared to an input way inputting both ofthe Tset and the Tr.

Here, it may need to input both of the setpoint temperature Tset and theinternal air temperature Tr to detect whether the operation area is in atransition area, which is just after the air-conditioning device starts(the internal air temperature is changing to the setpoint temperature),or the normal operation area. However, in this embodiment, thiscondition (the operation area is in the normal operation area) can bedetected from the fact that the temperature difference TD between theinternal air temperature Tr and the setpoint temperature Tset reachessubstantially zero. Therefore, this embodiment can easily detect whetherthe transition area or the normal operation by inputting the temperaturedifference TD.

Since the number of input is decreased, the number of the layer in theneural network can be also decreased, the link coefficient can bedecreased by about 25%, and the total learning time for calculating thelink coefficient 4106 can be decreased largely.

It is acceptable to input both the setpoint temperature Tset and theinternal air temperature Tr replaced with the temperature difference TDto the neural network 4100, if an increasing of the number of the inputwould be less than allowable condition.

(Twelfth Embodiment)

The twelfth embodiment will be explained with reference to FIG. 69. Inthis embodiment, the radiation correction coefficient calculatingportion 4029 shown in FIG. 57 and the radiation amount correctioncalculating portion 4030 are combined, and the combined calculatingportion is formed by one neural network 4200. That is, the neuralnetwork 4200 inputs signals including the temperature difference TD, theexternal air temperature Tam and the radiation amount Ts, and outputsthe radiation amount correction TAOS directly.

(Thirteenth embodiment)

In this embodiment, as shown in FIG. 70, the present invention isadopted to an air-conditioning device for automobiles, which is capableof controlling independently between a driver seat (Dr) sideair-conditioning zone and a passenger seat (Pa) side air-conditioningzone in a passenger compartment.

In an air-duct 4002, a partitioning wall 4002 a is arranged from theheater core 4005 portion to its downstream side in the air duct 4002 todivide it into a Dr side air passage 4002 b and a Pa side air passage4002 c.

A Dr side air-mixing door 4061 is arranged at the upstream side of theheater core 4005 in the Dr side air passage 4002 b. The Dr sideair-mixing door 4061 adjusts a ratio of two air amount in the Dr sideair passage 4002 b, an amount of heated air passed through the heatercore 4005 and an amount of cooled air by-passed the heater core 4005. APa side air-mixing door 4062 is arranged at the upstream side of theheater core 4005 in the Pa side air passage 4002 c, and adjusts a ratioof two air amount in the Pa side air passage 4002 c. Here, the two airamount are an amount of heated air passed through the heater core 4005and an amount of cooled air by-passed the heater core 4005.

At the most downstream side of the Dr side air passage 4002 b, a footblow-out port 4008 a, face blow-out ports 4009 a, 4009 b are provided.At the most downstream side of the Pa side air passage 4002 c, a footblow-out port 4008 b, face blow-out ports 4009 c, 4009 d are provided.Blowout port change-over doors 4011 a, 4012 a are arranged toselectively open/close the Dr side foot blow-out port 4008 a and theface blow-out ports 4009 a, 4009 b. Blowout port change-over doors 4011b, 4012 b are arranged to selectively open/close the Pa side footblow-out port 4008 b and the face blow-out ports 4009 c, 4009 d. Adefroster blow-out port 4010 and its blow-out port change-over door 4013are provided in the same way as the ninth embodiment.

The Dr side air-mixing door 4061 and the Pa side air-mixing door 4062are driven by independent servomotors 4015 and 4015 a. The Dr sideblow-out port change-over doors 4011 a, 4012 a and the Pa side blow-outport change-over doors 4011 b, 4012 b are driven by independentservomotors 4160 and 4170. Each of predetermined blow-out mode, such asa FACE mode (FACE), a BI-LEVEL mode (B/L mode), and a FOOT mode (FOOT)or the like can be set with respective to each ports 4002 b, 4002 c,independently, by changing the open/close condition of the blow-out portchange-over doors 4011 a, 4012 a, 4011 b and 4012 b.

A Dr side temperature setter (first temperature setter) 4021 a isprovided to set a setpoint temperature Tset(Dr) of the Dr sideair-conditioning zone relative to the Dr side air passage 4002 b, andoutput the setpoint temperature Tset(Dr) to the ECU 4019. A Pa sidetemperature setter (second temperature setter) 4021 b is provided to seta setpoint temperature Tset(Pa) of the Pa side air-conditioning zonerelative to the Pa side air passage 4002 c, and output the setpointtemperature Tset(Pa) to the ECU 4019. Both temperature setters 4021 b,4021 c are provided independently each other.

A Dr side radiation sensor 4024 a is arranged to detect a radiationamount TsDr to the Dr side air-conditioning zone, and a Pa sideradiation sensor 4024 b is arranged to detect a radiation amount TsPa tothe Pa side air-conditioning zone.

As temperature data detectors, an internal air sensor 4022 is arrangeddetect an internal air temperature Tr. An external air sensor 4023 isarranged to detect an external air temperature Tam. An evaporatortemperature sensor 4025 is arranged to detect a cooling temperature(blow-out air temperature) of the evaporator 4004. A water temperaturesensor 4026 is arranged to detect a temperature Tw of hot water flowinginto the heater core 4005.

Control functions processed by the microcomputer in the ECU 4019 areexecuted independently between the Dr side and the Pa side. The controlfunctions include Dr side and Pa side temporary target temperaturecalculating portions 4028, Dr side and Pa side radiation correctioncoefficient calculating portions 4029, Dr side and Pa side radiationamount correction calculating portions 4030, Dr side and Pa side targetblow-out temperature calculating portions 4031, Dr side and Pa sideblow-out port mode calculating portions 4034, Dr side and Pa sideair-mixing door opening degree calculating portions and so on.

Therefore, one of the neural networks 4100, which constitute each Drside radiation correction coefficient calculating portion 4029, inputssignals including the external air temperature Tam, Dr side temperaturedifference TDDr (=Tset(Dr)−TrDr), and Dr side radiation amount TsDr, andcalculates Dr side radiation correction coefficient FsDr. Similarly,another of the neural networks 4100, which constitute each Pa sideradiation correction coefficient calculating portion 4029, inputssignals including the external air temperature Tam, Pa side temperaturedifference TDPa (=Tset(Pa)−TrPa), and Pa side radiation amount TsPa, andcalculates Pa side radiation correction coefficient FsPa.

Here, in this embodiment, it is acceptable to control both the Dr sideblow-out port mode and the Pa side blow-out port mode in the same way.That is, it does not need to control independently. In that case, theblow-out port mode signal TMODE is calculated based on an average TAOX,which is an averaged value of a Dr side target blow-out temperatureTAO(Dr) and a Pa side target blow-out temperature TAO(Pa). Similarly,the Dr side suction port mode and the Pa side suction port mode arecontrolled in the same way by using the same calculating procedure.

The ninth to thirteenth embodiments can be also modified as follows.

(1) In the above-described embodiment, in addition to the radiationcorrection coefficient calculating portion 4029, it is acceptable toform the other calculating portions by neural networks. Here, the othercalculating portions include such as the temporary target temperaturecalculating portion 4028, the target blow-out temperature calculatingportion 4031, the air amount calculating portion 4032, the suction portmode calculating portion 4034.

(2) The learning method of the neural network can be replaceable withSemi Newton method or the like instead of the above-mentioned backpropagation method.

(3) The temperature difference TD can be obtained by using just Tr andTset to calculate (Tr−Tset), or using another factor in addition to Trand Tset.

(4) The air-mixing doors 4006, 4061, 4062 are arranged to adjust the mixratio of cooled air and heated air as the temperature adjuster. However,it is replaceable with a hot water valve to control an amount of hotwater flowing into the heater core 4005 or a temperature of the hotwater.

(5) As the temperature setter 4021, 4021 a, 4021 b for setting thetemperature of the air-conditioning zones, it can be used an analogdisplay in which indicates the temperature without digital figures,e.g., the temperature is indicated by colors.

(6) The present invention can be adaptable to another air-conditioningdevice, which has plurality of air-conditioning zone, and is needed tocontrol each of temperature of blow-out air to each zones independently.

(7) The neural networks 3100-3400 are replaceable with recurrent typeneural networks.

What is claimed is:
 1. An air-conditioning device, comprising: an airpassage in which air flows; a blower disposed in the air passage; a heatexchanger disposed in the air passage, for exchanging heat with airflowing through the air passage; a face blow-out port arranged at adownstream side of the air passage, for blowing air in a FACE mode; afoot blow-out port arranged at a downstream side of the air passage, forblowing air in a FOOT mode; a temperature setter to set a room setpointtemperature; a temperature data detector to detect temperature dataincluding a room internal air temperature and a room external airtemperature; a radiation amount detector to detect a radiation amount tothe room; and an air amount calculating portion including a neuralnetwork, for calculating an air amount blown by the blower, wherein theair amount calculating portion receives input signals including the roomsetpoint temperature, the room internal air temperature, the roomexternal air temperature and the radiation amount, independentlycalculates an air amount in the FACE mode and an air amount in the FOOTmode via the neural network, selects one of the air amount in the FACEmode and the FOOT mode, and outputs a final air amount based on a modesignal input thereto.
 2. An air-conditioning device according to claim1, wherein both of the face blow-out port and the foot blow-out portoutput air in a B/L mode, and a control characteristic of the air amountat the B/L mode is equal to that of the FACE mode.
 3. Anair-conditioning device according to claim 1, wherein the neural networkinputs a previously calculated temperature difference between the roominternal air temperature and the room setpoint temperature.
 4. Anair-conditioning device according to claim 1, wherein the foot blow-outport outputs air to the foot of a passenger, and the face blow-out portoutputs air to an upper body of the passenger.
 5. An air-conditioningdevice according to claim 1, wherein: the air passage includes a driverseat side air passage for a driver seat side air-conditioning zone and apassenger seat side air passage for a passenger seat sideair-conditioning zone, a driver seat side temperature adjuster and apassenger seat side temperature adjuster are arranged in the driver seatside air passage and the passenger seat side air passage, respectively,the face blow-out port and the foot blow-out port are arranged in bothof the driver seat side air passage and the passenger seat side airpassage, respectively, and temperatures of the first and the secondair-conditioning zones are controlled independently by air blown fromboth the driver seat side air passage and the passenger seat side airpassage.
 6. An air-conditioning device, comprising: an air passage inwhich air flows; a blower disposed in the air passage; a heat exchangerdisposed in the air passage, for exchanging heat with air; a faceblow-out port disposed at a downstream side of the air passage, forblowing air in a FACE mode; a foot blow-out port disposed at adownstream side of the air passage, for blowing air in a FOOT mode; atemperature setter to set a room setpoint temperature; a temperaturedata detector to detect temperature data including a room internal airtemperature and a room external air temperature; a radiation amountdetector to detect a radiation amount to the room; and an air amountcalculating portion for calculating an air amount blown from the blower,wherein the air amount calculating portion receives input signalsincluding the room setpoint temperatures, the room internal airtemperature, the room external air temperature and the radiation amount,reduces the air amount blown by the blower in proportion to lowering ofthe room internal air temperature, wherein: when an air-conditioningload based on the room external air temperature and the radiation amountis large, a decreasing point, at which the amount of air blown by theblower starts to decrease from a maximum area to the small amount area,is set to a high temperature side with respect to the room internal airtemperature, and when an air-conditioning load based on the roomexternal air temperature and the radiation amount is small, thedecreasing point is set to a low temperature side with respect to theroom internal air temperature.
 7. An air-conditioning device accordingto claim 6, wherein the air amount calculating portion includes a neuralnetwork.
 8. An air-conditioning device, comprising: an air passage inwhich air flows; a first and second blow-out port disposed at adownstream side in the air passage, for blowing conditioned air; atemperature setter to set a room setpoint temperature; a surroundingdata detector to detects a surrounding physical quantity data in theroom; an air amount calculating portion includes a neural network, forcalculating an air amount blown to the room, wherein the air amountcalculating portion receives the surrounding physical quantity data asinput data, and independently calculates an air amount to be supplied tothe first and the second air passages, via the neural network.
 9. Anair-conditioning device, comprising: an air passage in which air flows;a blower disposed in the air passage; a heat exchanger disposed in theair passage, to exchange heat with air; a plurality of blow-out portsdisposed at a downstream side in the air passage, to blow air todifferent directions; a blow-out port changer to control an opening anda closing of each of the blow-out ports; a temperature adjuster toadjust temperatures of air blown from the blow-out ports; a temperaturesetter to set a room setpoint temperature; a temperature data detectorto detect temperature data including a room internal air temperature anda room external air temperature; a target blow-out temperaturecalculating portion that receives the room setpoint temperature and thetemperature data, and calculates a target blow-out temperature of airblown to the room; a blow-out port mode calculating portion including aneural network, that receives the target blow-out temperature and a heatindication factor that influences a heating condition as input data, andcalculates a blow-out port mode signal to control the blow-out portchanger via the neural network.
 10. An air-conditioning device accordingto claim 9, wherein the heat indication factor includes a radiationamount in the room.
 11. An air-conditioning device according to claim 9,wherein: the heat exchanger includes a heating heat exchanger forheating air by using hot water, and the heat indication factor includesthe room external air temperature and a hot water temperature.
 12. Anair-conditioning device according to claim 9, wherein the heatindication factor includes the room internal air temperature.
 13. Anair-conditioning device according to claim 9, wherein the heatindication factor includes user skin temperature.
 14. Anair-conditioning device according to claim 9, wherein the signalindicating surrounding factor includes the room external airtemperature.
 15. An air-conditioning device for automobile, comprising:an air passage in which air flows; a blower disposed in the air passage;a heat exchanger disposed in the air passage, to exchange heat with air;a foot blow-out port disposed at a downstream side in the air passage,for blowing air in a FOOT mode; a face blow-out port disposed at adownstream side in the air passage, for blowing air in a FACE mode; ablow-out port changer to control an opening and a closing of each theblow-out ports; a temperature adjuster to adjust temperatures of airblown from the blow-out ports; a temperature setter to set a roomsetpoint temperature; a temperature data detector to detects temperaturedata including a room internal air temperature and a room external airtemperature; a target blow-out temperature calculating portion thatreceives the room setpoint temperature and the temperature data, andcalculates a target blow-out temperature of air blown to the room; and ablow-out port mode calculating portion includes a neural network, thatreceives the target blow-out temperature and a heat indication factorthat influences a heating condition, and calculates a blow-out port modesignal to control the blow-out port changer via the neural network,wherein the blow-out port mode signal includes a FOOT mode signal forblowing air from the foot blow-out port, a FACE mode signal for blowingair from the face blow-out port, and a B/L mode signal for blowing airfrom both the foot blow-out port and the face blow-out port.